Tag: StrandsAgents

Removing Clickbait from News Articles with an AI Agent, Python, Strands Agents, and AWS Bedrock

~ Gonzalo Ayuso ~ Leave a comment

The web is full of articles that do not want to tell you what happened too soon. The headline hints at something. The first paragraphs add suspense. The useful information is somewhere below the fold, after the cookie banner, the newsletter box, a couple of related links, and enough scrolling to make the advertising model happy.

That is annoying when all we want is the news.

That’s my PoC. A small command-line application that receives the URL of a news article, converts the page into clean Markdown, and asks an AI agent to rewrite it as clear journalism: direct headline, concise lead, short paragraphs, no clickbait.

The idea is simple:

plainnews rewrite "https://example.com/news/article"

The CLI does not scrape the page directly. It gives the URL to a Strands Agent. The agent has one tool, fetch_url_as_markdown, and the model decides when to use it. Once the article is available as Markdown, the agent rewrites it following a focused system prompt.

The architecture

The flow is straightforward:

The important part is the boundary between the agent and the tool. Fetching a web page, removing navigation, and converting HTML into Markdown is deterministic Python code. Deciding how to rewrite the story is the LLM’s job.

This keeps the PoC small and easy to reason about.

Project structure

I like to keep configuration in settings.py. It is a pattern I borrowed years ago from Django and I still use it in small prototypes because it keeps things simple:

src/
  cli.py
  settings.py
  commands/
    rewrite.py
  lib/
    agent.py
    prompts.py
    tools.py
    ui.py
  env/
    local/
      .env.example
tests/

The responsibilities are intentionally small:

  • src/commands/rewrite.py contains the Click command.
  • src/lib/tools.py contains the Strands tool and the HTML-to-Markdown pipeline.
  • src/lib/agent.py wires Strands Agents with AWS Bedrock.
  • src/lib/prompts.py keeps the editor prompt and the user task prompt.
  • src/lib/ui.py renders Markdown in the terminal with Rich.

Fetching a URL as Markdown

The agent only gets one tool. It fetches the URL, removes noisy page elements, selects the main content, converts it to Markdown, and truncates the result to 100K characters:

@tool
def fetch_url_as_markdown(url: str) -> str:
    """
    Fetch an HTTP or HTTPS URL, remove navigation, ads, scripts and layout noise,
    extract the main article content, convert it to Markdown, and return up to
    100K characters of clean text.
    Use this tool when the user pastes a URL or asks you to analyze a web page.
    """
    return fetch_url_as_markdown_impl(url)
def clean_html_to_markdown(html: str, *, max_chars: int = 100_000) -> str:
    soup = BeautifulSoup(html, "html.parser")
    for selector in NOISY_SELECTORS:
        for tag in soup.select(selector):
            tag.decompose()
    content = soup.find("main") or soup.find("article") or soup.body
    if content is None:
        return ""
    markdown = md(str(content), heading_style="ATX", bullets="-", strip=["a"])
    markdown = normalize_markdown(markdown)
    if len(markdown) > max_chars:
        return markdown[:max_chars].rstrip() + "\n\n[Content truncated]"
    return markdown

I am not trying to build a perfect browser engine here. This is a PoC. The goal is to get enough readable article content for the agent to work with. For many news pages, removing scripts, navigation, cookie boxes, newsletter blocks, related links and advertising containers is enough.

The agent

The agent uses Claude on AWS Bedrock through Strands Agents:

def create_agent(*, settings: Settings) -> Agent:
    boto_session = create_boto_session(settings)
    return Agent(
        model=BedrockModel(
            boto_session=boto_session,
            model_id=settings.resolved_bedrock_model_id,
        ),
        tools=[fetch_url_as_markdown],
        system_prompt=SYSTEM_PROMPT,
    )

The system prompt is the editorial policy. It tells the model to preserve only facts supported by the fetched article, answer in the requested output language, put the most important information first, remove suspense and filler, and write in a neutral tone.

The output format is Markdown:

  • a direct H1 headline
  • a concise lead paragraph
  • short factual paragraphs
  • a final What changed section, translated to the requested output language, explaining what noise was removed

That last section is useful during development. It gives us a quick sanity check: did the model actually remove clickbait, or did it just paraphrase the article?

The CLI

The command is intentionally small:

@click.command(name="rewrite")
@click.argument("url")
@runtime_options
def rewrite_command(
    url: str,
    aws_profile: str | None,
    region: str | None,
    model: str | None,
    language: str,
) -> None:
    if not is_supported_url(url):
        raise click.ClickException("URL must start with http:// or https://")
    settings = resolve_settings(
        aws_profile=aws_profile,
        aws_region=region,
        bedrock_model_id=model,
    )
    agent = create_agent(settings=settings)
    result = agent(build_rewrite_prompt(url, language=language))
    print_result("PlainNews", str(result))

The CLI validates the URL, creates the agent, sends the URL in the prompt, and renders the final Markdown with Rich.

The tool is not called manually from the command. That is the point of this PoC: the URL is part of the task, and the agent decides to call fetch_url_as_markdown because the tool description says it should be used when the user pastes a URL or asks to analyze a web page.

Usage

Run the command:

poetry run plainnews rewrite "https://example.com/news/article"

By default, PlainNews writes the rewritten article in English. You can choose a different output language with --language:

poetry run plainnews rewrite "https://example.com/news/article" --language Spanish

The output is rendered as Markdown in the terminal.

Example terminal output:

Tech stack

  • Python with Poetry
  • Strands Agents for tool-based agent orchestration
  • AWS Bedrock for the LLM runtime
  • BeautifulSoup for HTML cleanup
  • markdownify for HTML-to-Markdown conversion
  • Click for the command-line interface
  • Rich for Markdown terminal rendering
  • pytest for tests

A couple of notes

This is not a product and it is not a universal paywall remover. It is a small agentic workflow for a very specific frustration: articles that make readers work too hard to understand the basic facts.

Even in this small version, the pattern is useful: deterministic Python code prepares clean context, and the AI agent performs the editorial rewrite with a tight prompt.

And that’s all. Full source code available on GitHub.

AI Eurobeat Producer: Generating Music in Real-Time with AI Agents, Python, and MIDI

~ Gonzalo Ayuso ~ Leave a comment

What if you could describe the music you want to hear and have an AI produce it in real-time, sending MIDI notes directly to your DAW? That’s exactly what I built: a Python application that uses AI agents to generate Eurobeat and 90s techno patterns, outputting them as live MIDI to Akai’s MPC Beats.

I’m not a musician. I enjoy playing guitar from time to time, but I have zero experience with music production software. However, I’m gifted myself a Akai MPK mini Plus MIDI controller, which has 8 knobs and 8 pads, and I experimented with using it to control a music generation agent. No idea what I’m doing, but it’s fun.

As Akai MIDI controller can be connected to a laptop, and there I’ve got Python, this saturday morning I decided to build a simple prototype that connects an AI agent to MIDI output. The idea is simple. You write a prompt like “Energetic eurobeat in Am, Daft Punk style”, and an AI agent powered by Claude on AWS Bedrock generates patterns for 8 tracks: two drum kits, bass, rhodes, pluck, pad, and a lead melody. The patterns are sent as MIDI messages to MPC Beats, where each track is routed to a different virtual instrument. You can then modify the music live by writing new instructions, and use the physical knobs and pads on an Akai MPK Mini Plus to mute/unmute tracks, regenerate patterns, or reset the session.

I’m using the MPC Beats because it’s free and has a simple MIDI setup, but in theory this could work with any DAW that accepts MIDI input. The whole system is built in Python using Strands Agents for the AI orchestration, mido + python-rtmidi for MIDI I/O, and Rich for the terminal UI.

The Architecture

The flow is straightforward:

Project Structure

src/
  settings.py           # Configuration: BPM, tracks, MIDI devices
  cli.py                # Click CLI entry point
  commands/play.py      # Main play command
  agent/
    prompts.py          # System prompts for the AI producer
    tools.py            # PatternStore + @tool functions
    factory.py          # Agent creation
  midi/
    device.py           # MIDI device detection
    melody_player.py    # Threaded melody loop player
    drum_player.py      # Threaded drum loop player
  session/
    state.py            # State machine (IDLE/GENERATING/PLAYING)
    session.py          # Session orchestrator
  ui/
    menu.py             # Interactive terminal menu

Configuration

Everything starts with settings.py. The MIDI devices and AWS region are loaded from environment variables, while the musical parameters are defined as constants:

BPM = 122
BAR_DURATION = round((60 / BPM) * 4, 3)
LOOP_BARS = 4
LOOP_DURATION = round(BAR_DURATION * LOOP_BARS, 3)
TRACKS = {
    1: {"name": "Drums",         "channel": 0, "type": "drums"},
    2: {"name": "Drums Detroit", "channel": 1, "type": "drums"},
    3: {"name": "Rhodes",        "channel": 2, "type": "melody"},
    4: {"name": "Pluck",         "channel": 3, "type": "melody"},
    5: {"name": "Bass",          "channel": 4, "type": "melody"},
    6: {"name": "Org Bass",      "channel": 5, "type": "melody"},
    7: {"name": "Pad",           "channel": 6, "type": "melody"},
    8: {"name": "Lead",          "channel": 7, "type": "melody"},
}

Each track maps to a MIDI channel. Tracks 1-2 are drum kits (offset-based timing), tracks 3-8 are melodic instruments (duration-based timing). The MPC Beats “House Template” provides the virtual instruments: a Classic drum kit, a Detroit percussion kit, Electric Rhodes, Tube Pluck, Bassline, Organ Bass, Tube Pad, and an Instant Go lead synth.

The Bridge Between AI and MIDI: PatternStore and Tools

The core of the system is the PatternStore, a simple shared store where the AI writes patterns and the MIDI players read them:

class PatternStore:
    def __init__(self):
        self._patterns: dict[int, list] = {}
    def set(self, track_id: int, pattern: list) -> None:
        self._patterns[track_id] = pattern
    def get(self, track_id: int) -> list | None:
        return self._patterns.get(track_id)
    def clear(self) -> None:
        self._patterns.clear()

The Strands @tool functions are created via a factory that closes over the store:

def create_tools(store: PatternStore) -> list:
    @tool
    def set_melody_pattern(track_id: int, pattern: str) -> str:
        """Define a melodic line for a specific track."""
        data = json.loads(pattern)
        store.set(track_id, data)
        total = sum(n["duration"] for n in data)
        name = TRACKS[track_id]["name"]
        return f"OK - {name}: {len(data)} notes, total duration {total:.3f}s"
    @tool
    def set_drum_pattern(track_id: int, pattern: str) -> str:
        """Define a drum pattern for a specific drum track."""
        data = json.loads(pattern)
        store.set(track_id, data)
        name = TRACKS[track_id]["name"]
        return f"OK - {name}: {len(data)} hits"
    return [set_drum_pattern, set_melody_pattern]

A melody pattern is a JSON array of {note, duration, velocity} objects where the sum of durations must equal LOOP_DURATION (4 bars). A drum pattern uses {note, velocity, offset} where offset is the time in seconds from the loop start. The note value -1 represents silence, which is crucial for creating space in the arrangement.

The Agent

The agent is a Strands Agent using Claude Sonnet on AWS Bedrock. The system prompt is heavily detailed with music production instructions: frequency ranges for each track, velocity guidelines, and structural rules. The key instruction is “less is more” – not all tracks should play notes all the time:

def create_agent(store: PatternStore) -> Agent:
    return Agent(
        model=BedrockModel(
            model_id=Models.CLAUDE_SONNET,
            region_name=AWS_REGION,
        ),
        tools=create_tools(store),
        system_prompt=SYSTEM_PROMPT,
        callback_handler=None,
    )

There are two agents: one for initial generation (calls all 8 tools) and one for live modifications (only modifies the tracks that need to change). A third, lighter agent using Haiku generates the menu suggestions to keep latency and cost low.

MIDI Players

Two player classes handle the actual MIDI output. The MelodyLoopPlayer iterates through note events with durations:

def _loop(self, melody: list):
    while not self.stop_event.is_set():
        current = self.store.get(self.track_id) or melody
        for ev in current:
            if self.stop_event.is_set():
                break
            note = ev["note"]
            vel = ev.get("velocity", 80)
            if note >= 0:
                self._send("note_on", note=note, velocity=vel, channel=self.channel)
            deadline = time.time() + ev["duration"]
            while not self.stop_event.is_set() and time.time() < deadline:
                time.sleep(0.02)
            if note >= 0:
                self._send("note_off", note=note, velocity=0, channel=self.channel)

The DrumLoopPlayer uses offset-based timing instead, scheduling hits at specific points within the loop. Both players read from the PatternStore on each loop iteration, which enables hot-swapping patterns during live modifications.

The Session

The Session class orchestrates everything. It manages the state machine (IDLE -> GENERATING -> PLAYING), owns the PatternStore, creates the agents, and handles MIDI input from the controller:

class Session:
    def __init__(self):
        self.state = State.IDLE
        self.store = PatternStore()
        self.agent = create_agent(self.store)
        self.live_agent = create_live_agent(self.store)
        self._agent_busy = threading.Lock()

When generation completes, playback starts with a progressive intro – tracks are unmuted one by one with a 2-bar delay between each, creating a build-up effect:

def _start_playback(self):
    self.state = State.PLAYING
    for tid in TRACKS:
        self.players[tid].muted = True
        self.players[tid].start(patterns[tid])
    intro_delay = BAR_DURATION * 2
    for i, tid in enumerate(INTRO_ORDER):
        timer = threading.Timer(intro_delay * i, self._unmute_track, args=(tid,))
        timer.start()

How It Works

  1. Run python cli.py play
  2. The app detects your MPK Mini Plus and shows a menu with AI-generated suggestions
  3. Select a suggestion or write your own prompt
  4. The AI generates 8 track patterns (takes a few seconds)
  5. Playback begins with a progressive build-up
  6. Write new instructions to modify the music live
  7. Use knobs K1-K8 to mute/unmute individual tracks
  8. PAD 1 regenerates with the same prompt, PAD 2 resets everything

Tech Stack

  • Python 3.13 with Poetry
  • Strands Agents for AI agent orchestration
  • AWS Bedrock (Claude Sonnet + Haiku) for pattern generation
  • mido + python-rtmidi for MIDI I/O
  • Akai MPK Mini Plus as MIDI controller
  • MPC Beats as the DAW/sound engine
  • Rich for terminal UI
  • Click for CLI

And that’s all. Full source code available on GitHub.

Predicting the future: time series forecasting with AI Agents and Amazon Chronos-Bolt

~ Gonzalo Ayuso ~ Leave a comment

Predicting the future is something we all try to do. Whether it’s energy consumption, sensor readings, or production metrics, having a reliable forecast helps us make better decisions. The problem is that building a good forecasting model traditionally requires deep statistical knowledge, and a lot of tuning. What if we could just hand our data to an AI agent and ask “what’s going to happen next”?

That’s exactly what this project does. It combines Strands Agents with Amazon Chronos-Bolt, a foundation model for time series forecasting available on AWS Bedrock Marketplace, to create an AI agent that can forecast any numerical time series through natural language.

The architecture

The idea is simple. We have a Strands Agent powered by Claude (via AWS Bedrock) that understands natural language. When the user asks for a forecast, the agent calls a custom tool that invokes Chronos-Bolt to generate predictions. The agent then interprets the results and explains them in plain language.

The key here is that the agent doesn’t just return raw numbers. It understands the context, explains trends, and presents the confidence intervals in a way that makes sense.

The forecast tool

The tool is defined using the @tool decorator from Strands. This decorator turns a regular Python function into something the agent can discover and invoke on its own:

@tool
def forecast_time_series(
    values: Annotated[
        list[float],
        "Historical time series values in chronological order. "
        "Values should be evenly spaced (e.g., hourly, daily). Minimum 10 values.",
    ],
    prediction_length: Annotated[
        int,
        "Number of future steps to predict. "
        "Uses the same time unit as the input data.",
    ],
    quantile_levels: Annotated[
        Optional[list[float]],
        "Quantile levels for confidence intervals. Default: [0.1, 0.5, 0.9]. "
        "0.5 is the median forecast, 0.1 and 0.9 define the 80% confidence band.",
    ] = None,
) -> dict:

The Annotated type hints serve a dual purpose: they validate types at runtime and provide descriptions that the LLM reads to understand how to use the tool. This means the agent knows it needs a list of floats, a prediction length, and optionally custom quantile levels, all from the type annotations alone.

The tool validates the input (minimum 10 values, maximum 50,000, prediction length between 1 and 1,000), filters out NaN values, and then calls the Chronos-Bolt client:

result = invoke_chronos(
    values=clean_values,
    prediction_length=prediction_length,
    quantile_levels=quantile_levels,
)
return {
    "status": "success",
    "content": [{"text": "\n".join(summary_lines)}],
    "metadata": {
        "quantiles": result.quantiles,
        "prediction_length": result.prediction_length,
        "history_length": result.history_length,
    },
}

The response includes both a human-readable summary (in content) and the raw quantile data (in metadata), so the agent can reference exact numbers when explaining the forecast.

The Chronos-Bolt client

Chronos-Bolt is accessed through the Bedrock runtime API. The client sends the historical values and receives predictions at different quantile levels:

def invoke_chronos(
    values: list[float],
    prediction_length: int,
    quantile_levels: list[float] | None = None,
) -> ForecastResult:
    client = _get_bedrock_runtime_client()
    payload = {
        "inputs": [{"target": values}],
        "parameters": {
            "prediction_length": prediction_length,
            "quantile_levels": quantiles,
        },
    }
    response = client.invoke_model(
        modelId=CHRONOS_ENDPOINT_ARN,
        body=json.dumps(payload),
        contentType="application/json",
        accept="application/json",
    )

The invoke_model call uses the SageMaker endpoint ARN deployed through Bedrock Marketplace. Chronos-Bolt returns predictions organized by quantile levels, by default, the 10th, 50th (median), and 90th percentiles. This gives us not just a single forecast line, but a confidence band: the 80% interval between the 10th and 90th percentiles tells us how uncertain the model is about its predictions.

The Bedrock runtime client is configured with generous timeouts (120s read, 30s connect) and automatic retries, since inference on time series data can take a moment depending on the history length:

def _get_bedrock_runtime_client():
    return boto3.client(
        "bedrock-runtime",
        region_name=AWS_REGION,
        config=Config(
            read_timeout=120,
            connect_timeout=30,
            retries={"max_attempts": 3},
        ),
    )

The agent

Wiring everything together is straightforward. We create a BedrockModel pointing to Claude and pass our forecast tool to the Agent:

from strands import Agent
from strands.models.bedrock import BedrockModel
from settings import AWS_REGION, Models
from forecast import forecast_time_series
SYSTEM_PROMPT = """You are a time series forecasting assistant powered by Amazon Chronos-Bolt.
You help users predict future values from historical numerical data. When a user provides
time series data or describes a scenario, use the forecast_time_series tool to generate
predictions.
When presenting results:
- Show the median forecast (quantile 0.5) as the main prediction
- Explain the confidence band (quantiles 0.1 and 0.9) as the uncertainty range
- Summarize trends in plain language
"""
def create_agent() -> Agent:
    bedrock_model = BedrockModel(
        model_id=Models.CLAUDE_SONNET,
        region_name=AWS_REGION,
    )
    return Agent(
        model=bedrock_model,
        system_prompt=SYSTEM_PROMPT,
        tools=[forecast_time_series],
    )

The system prompt is important here. It tells Claude that it has forecasting capabilities and how to present the results. Without it, the agent would still call the tool correctly (thanks to the Annotated descriptions), but it might not explain the confidence bands or summarize trends as clearly.

Running it

The CLI entry point (cli.py) registers commands and wires everything together. The forecast command generates synthetic hourly data (a sine wave with noise) by default and asks the agent to forecast. You can also pass a custom prompt.

The entry point is minimal:

import click
from commands.forecast import run as forecast
@click.group()
def cli():
    pass
cli.add_command(cmd=forecast, name="forecast")
if __name__ == "__main__":
    cli()

The actual command lives in commands/forecast.py:

@click.command()
@click.option("--prompt", "-p", default=None, help="Custom prompt for the agent.")
def run(prompt: str | None):
    agent = create_agent()
    if prompt is None:
        values = generate_sample_data(num_points=100)
        values_str = ", ".join(f"{v:.2f}" for v in values)
        prompt = (
            f"I have the following hourly sensor readings from the last 100 hours:\n"
            f"[{values_str}]\n\n"
            f"Please forecast the next 24 hours and explain the predicted trend."
        )
    response = agent(prompt)
    click.echo(response)

The sine wave is a good choice for a demo because it has a clear periodic pattern that Chronos-Bolt should capture well. With 100 hours of history (about 4 full cycles of a 24-hour pattern), the model has enough data to identify the periodicity and project it forward.

Example

(venv) ➜  src python cli.py forecast
2026-02-27 14:11:16,471 - INFO - Found credentials in shared credentials file: ~/.aws/credentials
2026-02-27 14:11:16,506 - INFO - Creating Strands MetricsClient
Sure! Let me run the forecast on your 100-hour sensor readings right away.
Tool #1: forecast_time_series
2026-02-27 14:11:22,981 - INFO - Starting forecast: history=100, prediction_length=24
2026-02-27 14:11:22,981 - INFO - Invoking Chronos-Bolt: history_length=100, prediction_length=24, quantiles=[0.1, 0.5, 0.9]
2026-02-27 14:11:22,994 - INFO - Found credentials in shared credentials file: ~/.aws/credentials
2026-02-27 14:11:23,697 - INFO - Forecast complete: 24 steps, 4 quantiles
Here are the results of your 24-hour forecast:

Script response

📊 24-Hour Sensor Forecast

Hour Low (10th %) Median (50th %) High (90th %)
+1 ~-0.29 ~0.85 ~0.99
+2 ~0.29 ~0.95 ~0.99
+3 ~0.49 ~0.99 ~0.99
+4 ~0.47 ~0.83 ~0.99
+5 ~0.24 ~0.72 ~0.96
+6 ~0.09 ~0.53 ~0.83
+7 ~-0.25 ~0.19 ~0.59
+8 ~-0.60 ~-0.22 ~0.25
+9 ~-0.91 ~-0.51 ~-0.04
+10 ~-0.99 ~-0.76 ~-0.30
+11 ~-0.99 ~-0.87 ~-0.46
+12 ~-0.99 ~-0.93 ~-0.56
+13 ~-0.99 ~-0.96 ~-0.57
+14 ~-0.99 ~-0.93 ~-0.52
+15 ~-0.99 ~-0.87 ~-0.44
+16 ~-0.99 ~-0.79 ~-0.35
+17 ~-0.96 ~-0.63 ~-0.16
+18 ~-0.86 ~-0.46 ~0.02
+19 ~-0.66 ~-0.24 ~0.21
+20 ~-0.44 ~-0.01 ~0.42
+21 ~-0.21 ~0.22 ~0.60
+22 ~-0.04 ~0.45 ~0.78
+23 ~0.16 ~0.66 ~0.90
+24 ~0.31 ~0.80 ~0.96

🔍 Trend Explanation

Your sensor data clearly exhibits a sinusoidal/oscillating pattern — cycling between peaks near +1.0 and troughs near -1.0 roughly every ~24 hours. The model has picked this up beautifully! Here’s what to expect:

  1. Hours +1 to +4 — Continued Peak 🔼: The signal is currently rising (ending at ~0.67–0.70), and the model predicts it will crest near ~+0.85 to +0.99 in the first few hours — completing the current positive phase.

  2. Hours +5 to +13 — Descent & Trough 🔽: The signal then descends sharply, hitting a trough around hours +12 to +14 with a median near -0.93 to -0.96 — matching the negative peaks seen in the historical data.

  3. Hours +14 to +24 — Recovery 🔼: After bottoming out, the signal climbs back up, reaching approximately +0.80 by hour +24, setting up the next positive cycle.

📐 Confidence Band

The 80% confidence interval (low–high columns) is relatively tight, reflecting the model’s high confidence in the periodic nature of this signal. The widest uncertainty occurs around the transition zones (hours +7–+9 and +17–+19), which is typical for oscillating signals near the zero-crossing points.

In short: your sensor is behaving like a clean oscillating signal with an ~24-hour period, and the next full cycle looks very consistent with historical behavior.Here are the results of your 24-hour forecast:

📊 24-Hour Sensor Forecast

Hour Low (10th %) Median (50th %) High (90th %)
+1 ~-0.29 ~0.85 ~0.99
+2 ~0.29 ~0.95 ~0.99
+3 ~0.49 ~0.99 ~0.99
+4 ~0.47 ~0.83 ~0.99
+5 ~0.24 ~0.72 ~0.96
+6 ~0.09 ~0.53 ~0.83
+7 ~-0.25 ~0.19 ~0.59
+8 ~-0.60 ~-0.22 ~0.25
+9 ~-0.91 ~-0.51 ~-0.04
+10 ~-0.99 ~-0.76 ~-0.30
+11 ~-0.99 ~-0.87 ~-0.46
+12 ~-0.99 ~-0.93 ~-0.56
+13 ~-0.99 ~-0.96 ~-0.57
+14 ~-0.99 ~-0.93 ~-0.52
+15 ~-0.99 ~-0.87 ~-0.44
+16 ~-0.99 ~-0.79 ~-0.35
+17 ~-0.96 ~-0.63 ~-0.16
+18 ~-0.86 ~-0.46 ~0.02
+19 ~-0.66 ~-0.24 ~0.21
+20 ~-0.44 ~-0.01 ~0.42
+21 ~-0.21 ~0.22 ~0.60
+22 ~-0.04 ~0.45 ~0.78
+23 ~0.16 ~0.66 ~0.90
+24 ~0.31 ~0.80 ~0.96

🔍 Trend Explanation

Your sensor data clearly exhibits a sinusoidal/oscillating pattern — cycling between peaks near +1.0 and troughs near -1.0 roughly every ~24 hours. The model has picked this up beautifully! Here’s what to expect:

  1. Hours +1 to +4 — Continued Peak 🔼: The signal is currently rising (ending at ~0.67–0.70), and the model predicts it will crest near ~+0.85 to +0.99 in the first few hours — completing the current positive phase.

  2. Hours +5 to +13 — Descent & Trough 🔽: The signal then descends sharply, hitting a trough around hours +12 to +14 with a median near -0.93 to -0.96 — matching the negative peaks seen in the historical data.

  3. Hours +14 to +24 — Recovery 🔼: After bottoming out, the signal climbs back up, reaching approximately +0.80 by hour +24, setting up the next positive cycle.

📐 Confidence Band

The 80% confidence interval (low–high columns) is relatively tight, reflecting the model’s high confidence in the periodic nature of this signal. The widest uncertainty occurs around the transition zones (hours +7–+9 and +17–+19), which is typical for oscillating signals near the zero-crossing points.

In short: your sensor is behaving like a clean oscillating signal with an ~24-hour period, and the next full cycle looks very consistent with historical behavior.

And that’s all! Full code in my GitHub account.

Transforming Raw Spreadsheets into Professional Excel Reports with AI Agents and Python

~ Gonzalo Ayuso ~ Leave a comment

We all deal with spreadsheets. They’re everywhere, financial reports, sales data, operational metrics. But raw data in a flat table is just that: raw data. To extract insights, you need dashboards, charts, KPIs, conditional formatting, and executive summaries. Doing this manually is tedious. What if an AI agent could take any raw .xlsx file and transform it into a professional, multi-sheet workbook with formulas, charts, and insights, automatically?

That’s exactly what this project does. The idea is simple: you give it a spreadsheet, and an AI agent running Python inside a AWS sandbox analyzes the data, builds a Dashboard with KPI formulas, formats the source data, generates an executive summary with real insights, and creates analysis sheets with charts, all using Excel formulas, never hardcoded values.

The two-agent pattern

The core of the system is a two-agent architecture. An outer orchestrator agent (Claude Sonnet) manages the workflow, while an inner agent (Claude Opus) does the actual Excel work inside an AWS Bedrock Code Interpreter sandbox. This separation keeps the orchestration clean and lets the inner agent focus entirely on writing Python code with openpyxl.

The CLI entry point uses Click. When you run the command, it creates the orchestrator agent with the xlsx_enhancer tool:

@click.command()
@click.argument("input_file", type=click.Path(exists=True))
@click.argument("output_file", type=click.Path(), required=False)
def run(input_file: str, output_file: str | None):
    if not output_file:
        p = Path(input_file)
        output_file = str(p.parent / f"enhanced_{p.name}")
    agent = create_agent(
        system_prompt=ORCHESTRATOR_PROMPT,
        tools=[xlsx_enhancer],
        hooks=[ToolProgressHook()],
    )
    response = agent(
        f"Process the Excel file at {input_file} and save the enhanced version to {output_file}"
    )
    click.echo(f"Done: {str(response)}")

The agent factory wraps the Strands SDK configuration, model selection, retry logic, sliding window conversation management:

def create_agent(
    system_prompt: str,
    model: str = Models.CLAUDE_45,
    tools: Optional[List[Any]] = None,
    hooks: Optional[List[HookProvider]] = None,
    temperature: float = 0.3,
    read_timeout: int = 300,
    connect_timeout: int = 60,
    max_attempts: int = 10,
    maximum_messages_to_keep: int = 30,
    should_truncate_results: bool = True,
    callback_handler: Any = None,
) -> Agent:
    bedrock_model = create_bedrock_model(
        model=model,
        temperature=temperature,
        read_timeout=read_timeout,
        connect_timeout=connect_timeout,
        max_attempts=max_attempts,
    )
    return Agent(
        system_prompt=system_prompt,
        model=bedrock_model,
        conversation_manager=SlidingWindowConversationManager(
            window_size=maximum_messages_to_keep,
            should_truncate_results=should_truncate_results,
        ),
        tools=tools,
        hooks=hooks,
        callback_handler=callback_handler,
    )

The xlsx_enhancer tool

This is the centerpiece. It’s a Strands @tool that orchestrates a 4-step pipeline: upload the file to the sandbox, run the inner agent, verify the output, and download the result from the sandbox.

@tool
def xlsx_enhancer(input_file: str, output_file: str, instructions: str = "") -> dict:
    """Enhance an Excel file with professional formatting, dashboards, charts, and analysis sheets."""
    input_path = Path(input_file)
    output_path = Path(output_file)
    if not input_path.exists():
        return XlsxResult(success=False, error=f"Input file not found: {input_file}").model_dump()
    if input_path.suffix.lower() != ".xlsx":
        return XlsxResult(success=False, error=f"Input file must be .xlsx, got: {input_path.suffix}").model_dump()
    user_prompt = USER_PROMPT
    if instructions.strip():
        user_prompt = f"{USER_PROMPT}\n\n## Additional Instructions\n{instructions}"
    try:
        code_tool = AgentCoreCodeInterpreter(region=AWS_REGION)
        sandbox = SandboxIO(code_tool)
        # 1. Upload
        sandbox.upload(input_path, SANDBOX_INPUT)
        # 2. Run the inner XLSX agent
        agent = create_agent(
            system_prompt=SYSTEM_PROMPT,
            model=Models.CLAUDE_46_OPUS,
            tools=[code_tool.code_interpreter],
        )
        response = agent(user_prompt)
        # 3. Verify output exists in sandbox
        if not sandbox.verify_exists(SANDBOX_OUTPUT):
            return XlsxResult(
                success=False,
                error=f"The XLSX agent did not produce '{SANDBOX_OUTPUT}'",
            ).model_dump()
        # 4. Download
        output_path.parent.mkdir(parents=True, exist_ok=True)
        sandbox.download(SANDBOX_OUTPUT, output_path)
        return XlsxResult(success=True, output_path=str(output_path)).model_dump()
    except SandboxIOError as e:
        return XlsxResult(success=False, error=f"Sandbox I/O failed: {e}").model_dump()

The inner agent receives two carefully crafted prompts. The system prompt enforces hard rules about Excel integrity, formulas instead of hardcoded values, sheet name constraints, error handling. The user prompt defines the exact structure: Dashboard with KPI formulas, formatted Data sheet, executive Summary with LLM-generated insights, and Analysis sheets with charts.

The formula-first philosophy

One of the most important design decisions is that the agent never hardcodes computed values in cells. Every number in the output workbook comes from an Excel formula:

# FORBIDDEN - Computing in Python
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes a value
# REQUIRED - Excel formulas
sheet['B10'] = '=SUM(Data!D:D)'
sheet['C10'] = '=SUMIF(Data!A:A,"Category",Data!B:B)'
sheet['D10'] = '=IFERROR(AVERAGEIF(Data!A:A,A10,Data!D:D),0)'

This means the resulting Excel file is alive, change a value in the Data sheet and every KPI, every analysis table, every chart updates automatically. The IFERROR wrapping prevents #DIV/0! errors that would otherwise break AVERAGEIF formulas when a category has no data.

Handling binary files in the sandbox

The AWS Bedrock Code Interpreter sandbox runs Python in an isolated environment. Uploading the source file is straightforward, the bedrock client handles binary blobs natively. But downloading the result is trickier: the download_file method decodes everything as UTF-8, which corrupts binary xlsx files.

The solution is to base64-encode the file inside the sandbox and extract the text from the stream:

class SandboxIO:
    def __init__(self, code_tool: AgentCoreCodeInterpreter):
        self._code_tool = code_tool
    def _get_client(self):
        session_name, error = self._code_tool._ensure_session(None)
        if error:
            raise SandboxIOError(f"Failed to ensure session: {error}")
        session_info = self._code_tool._sessions.get(session_name)
        return session_info.client
    def upload(self, local_path: Path, sandbox_name: str = "input.xlsx") -> None:
        file_bytes = local_path.read_bytes()
        client = self._get_client()
        client.upload_file(path=sandbox_name, content=file_bytes)
    def download(self, sandbox_name: str, local_path: Path) -> None:
        client = self._get_client()
        result = client.execute_code(
            "import base64, os\n"
            f"p = '{sandbox_name}'\n"
            "data = open(p, 'rb').read()\n"
            "print(base64.b64encode(data).decode())\n"
        )
        b64_text = _extract_stream_text(result)
        file_bytes = base64.b64decode(b64_text.strip())
        if not file_bytes.startswith(b"PK\x03\x04"):
            raise SandboxIOError("Downloaded file is not a valid xlsx")
        local_path.write_bytes(file_bytes)

The PK\x03\x04 check validates the ZIP magic bytes — every xlsx file is a ZIP archive internally.

The original xlsx file

This is the original file we feed into the agent. It’s a flat table with rows and columns. No formatting, no formulas, just bored raw data.

What the agent produces

Given a raw financial spreadsheet, the agent generates a multi-sheet workbook:

  • Dashboard: KPI cards with formulas (=SUM(Data!D:D), =COUNT(Data!A:A)), color-coded metrics, and a hyperlinked index to all sheets
  • Data: The original data with dark blue headers, alternating row colors, auto-filters, data bars on numeric columns, and frozen panes
  • Summary: An executive summary written by the LLM, key findings, concentration risks, trends, anomalies, and actionable recommendations
  • Analysis sheets: One per categorical column, each with a SUMIF/COUNTIF/AVERAGEIF table and a bar chart

The agent also detects the language of the input data and uses the same language for all generated content, sheet names, titles, labels, and the executive summary.

Monitoring tool execution

A simple hook tracks how long each tool execution takes. It can be extended to integrate with our application and provide real-time feedback to users about the agent’s progress:

class ToolProgressHook(HookProvider):
    def __init__(self) -> None:
        self._start_time: float = 0
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeToolCallEvent, self.on_tool_start)
        registry.add_callback(AfterToolCallEvent, self.on_tool_end)
    def on_tool_start(self, event: BeforeToolCallEvent) -> None:
        self._start_time = time.time()
        tool_name = event.tool_use.get("name", "unknown")
        logger.info("Tool started: %s", tool_name)
    def on_tool_end(self, event: AfterToolCallEvent) -> None:
        elapsed = time.time() - self._start_time
        tool_name = event.tool_use.get("name", "unknown")
        logger.info("Tool finished: %s (%.1fs)", tool_name, elapsed)

And that’s all. With tools like Strands Agents and AWS Bedrock’s Code Interpreter, we can build AI agents that go beyond text generation, they produce real, functional artifacts. A raw spreadsheet goes in, a professional report comes out. No templates, no manual formatting, just an agent that understands data and knows how to present it.

Full code in my github account.

Using Map-Reduce to process large documents with AI Agents and Python

~ Gonzalo Ayuso ~ Leave a comment

We live in the era of Large Language Models (LLMs) with massive context windows. Claude 3.5 Sonnet offers 200k tokens, and Gemini 1.5 Pro goes up to 2 million. So, why do we still need to worry about document processing strategies? The answer is yes, we do. For example, AWS Bedrock has a strict limit of 4.5MB for documents, regardless of token count. That’s means we can’t just stuff file greater than 4.5MB into a prompt. Today we’ll show you how I built a production-ready document processing agent that handles large files by implementing a Map-Reduce pattern using Python, AWS Bedrock, and Strands Agents.

The core idea is simple: instead of asking the LLM to “read this book and answer” we break the book into chapters, analyze each chapter in parallel, and then synthesize the results.

Here is the high-level flow:

The heart of the implementation is the DocumentProcessor class. It decides whether to process a file as a whole or split it based on a size threshold. We define a threshold (e.g., 4.3MB) to stay safely within Bedrock’s limits. If the file is larger, we trigger the _process_big method.

# src/lib/processor/processor.py

BYTES_THRESHOLD = 4_300_000

async def _process_file(self, file: DocumentFile, question: str, with_callback=True):
    file_bytes = Path(file.path).read_bytes()
    # Strategy pattern: Choose the right processor based on file size
    processor = self._process_big if len(file_bytes) > BYTES_THRESHOLD else self._process
    async for chunk in processor(file_bytes, file, question, with_callback):
        yield chunk

To increase the performance, we use asyncio to process the file in parallel and we use a semaphore to control the number of workers.

async def _process_big(self, file_bytes: bytes, file: DocumentFile, question: str, with_callback=True) -> AsyncIterator[str]:
    # ... splitting logic ...
    semaphore = asyncio.Semaphore(self.max_workers)

    # Create async tasks for each chunk
    tasks = [
        self._process_chunk(chunk, i, file_name, question, handler.format, semaphore)
        for i, chunk in enumerate(chunks, 1)
    ]

    # Run in parallel
    results = await asyncio.gather(*tasks)
    
    # Sort results to maintain document order
    results.sort(key=lambda x: x[0])
    responses_from_chunks = [response for _, response in results]

Each chunk is processed by an isolated agent instance that only sees that specific fragment and the user’s question. Once we have the partial analyses, we consolidate them. This acts as a compression step: we’ve turned raw pages into relevant insights.

def _consolidate_and_truncate(self, responses: list[str], num_chunks: int) -> str:
    consolidated = "\n\n".join(responses)
    
    if len(consolidated) > MAX_CONTEXT_CHARS:
        # Safety mechanism to ensure we don't overflow the final context
        return consolidated[:MAX_CONTEXT_CHARS] + "\n... [TRUNCATED]"
    return consolidated

Finally, we feed this consolidated context to the agent for the final answer. In a long-running async process, feedback is critical. I implemented an Observer pattern to decouple the processing logic from the UI/Logging.

# src/main.py

class DocumentProcessorEventListener(ProcessingEventListener):
    async def on_chunk_start(self, chunk_number: int, file_name: str):
        logger.info(f"[Worker {chunk_number}] Processing chunk for file {file_name}")

    async def on_chunk_end(self, chunk_number: int, file_name: str, response: str):
        logger.info(f"[Worker {chunk_number}] Completed chunk for file {file_name}")

By breaking down large tasks, we not only bypass technical limits but often get better results. The model focuses on smaller sections, reducing hallucinations, and the final answer is grounded in a pre-processed summary of facts.

We don’t just send text; we send the raw document bytes. This allows the model (Claude 4.5 Sonnet via Bedrock) to use its native document processing capabilities. Here is how we construct the message payload:

# src/lib/processor/processor.py

def _create_document_message(self, file_format: str, file_name: str, file_bytes: bytes, text: str) -> list:
    return [
        {
            "role": "user",
            "content": [
                {
                    "document": {
                        "format": file_format,
                        "name": file_name,
                        "source": {"bytes": file_bytes},
                    },
                },
                {"text": text},
            ],
        },
    ]

When processing chunks, we don’t want the model to be chatty. We need raw information extraction. We use a “Spartan” system prompt that enforces brevity and objectivity, ensuring the consolidation phase receives high-signal input.

# src/lib/processor/prompts.py

SYSTEM_CHUNK_PROMPT = f"""
You are an artificial intelligence assistant specialized in reading and analyzing files.
You have received a chunk of a large file.
...
If the user's question cannot be answered with the information in the current chunk, do not answer it directly.

{SYSTEM_PROMPT_SPARTAN}

The SYSTEM_PROMPT_SPARTAN (injected above) explicitly forbids conversational filler, ensuring we maximize the token budget for actual data.

The project handles pdf and xlsx files. The rest of the file types are not processed and are given to the LLM as-is.

With this architecture, we can process large files in a production environment. This allows us to easily plug in different interfaces, whether it’s a CLI logger (as shown) or a WebSocket update for a UI frontend like Chainlit.

Full code in my github

Building scalable multi-purpose AI agents: Orchestrating Multi-Agent Systems with Strands Agents and Chainlit

~ Gonzalo Ayuso ~ Leave a comment

We can build simple AI agents that handle specific tasks quite easily today. But what about building AI systems that can handle multiple domains effectively? One approach is to create a single monolithic agent that tries to do everything, but this quickly runs into problems of context pollution, maintenance complexity, and scaling limitations. In this article, we’ll show a production-ready pattern for building multi-purpose AI systems using an orchestrator architecture that coordinates domain-specific agents.

The idea is simple: Don’t build one agent to rule them all instead, create specialized agents that excel in their domains and coordinate them through an intelligent orchestrator. The solution is an orchestrator agent that routes requests to specialized sub-agents, each with focused expertise and dedicated tools. Think of it as a smart router that understands intent and delegates accordingly.

That’s the core of the Orchestrator Pattern for multi-agent systems:

User Query → Orchestrator Agent → Specialized Agent(s) → Orchestrator → Response

For our example we have three specialized agents:

  1. Weather Agent: Expert in meteorological data and weather patterns. It uses external weather APIs to fetch historical and current weather data.
  2. Logistics Agent: Specialist in supply chain and shipping operations. Fake logistics data is generated to simulate shipment tracking, route optimization, and delivery performance analysis.
  3. Production Agent: Focused on manufacturing operations and production metrics. Also, fake production data is generated to analyze production KPIs.

That’s the architecture in a nutshell:

┌─────────────────────────────────────────────┐
│          Orchestrator Agent                 │
│  (Routes &amp; Synthesizes)                 │
└────────┬─────────┬─────────┬────────────────┘
         │         │         │
    ┌────▼────┐ ┌──▼─────┐ ┌─▼─────────┐
    │ Weather │ │Logistic│ │Production │
    │  Agent  │ │ Agent  │ │  Agent    │
    └────┬────┘ └──┬─────┘ └┬──────────┘
         │         │        │
    ┌────▼────┐ ┌──▼─────┐ ┌▼──────────┐
    │External │ │Database│ │ Database  │
    │   API   │ │ Tools  │ │  Tools    │
    └─────────┘ └────────┘ └───────────┘

The tech stack includes:

  • AWS Bedrock with Claude 4.5 Sonnet for agent reasoning
  • Strands Agents framework for agent orchestration
  • Chainlit for the conversational UI
  • FastAPI for the async backend
  • PostgreSQL for storing conversation history and domain data

The orchestrator’s job is simple but critical: understand the user’s intent and route to the right specialist(s).

MAIN_SYSTEM_PROMPT = """You are an intelligent orchestrator agent 
responsible for routing user requests to specialized sub-agents 
based on their domain expertise.

## Available Specialized Agents

### 1. Production Agent
**Domain**: Manufacturing operations, production metrics, quality control
**Handles**: Production KPIs, machine performance, downtime analysis

### 2. Logistics Agent
**Domain**: Supply chain, shipping, transportation operations
**Handles**: Shipment tracking, route optimization, delivery performance

### 3. Weather Agent
**Domain**: Meteorological data and weather patterns
**Handles**: Historical weather, atmospheric conditions, climate trends

## Your Decision Process
1. Analyze the request for key terms and domains
2. Determine scope (single vs multi-domain)
3. Route to appropriate agent(s)
4. Synthesize results when multiple agents are involved
"""

The orchestrator receives specialized agents as tools:

def get_orchestrator_tools() -> List[Any]:
    from tools.logistics.agent import logistics_assistant
    from tools.production.agent import production_assistant
    from tools.weather.agent import weather_assistant

    tools = [
        calculator,
        think,
        current_time,
        AgentCoreCodeInterpreter(region=AWS_REGION).code_interpreter,
        logistics_assistant,  # Specialized agent as tool
        production_assistant,  # Specialized agent as tool
        weather_assistant     # Specialized agent as tool
    ]
    return tools

Each specialized agent follows a consistent pattern. Here’s the weather agent:

@tool
@stream_to_step("weather_assistant")
async def weather_assistant(query: str):
    """
    A research assistant specialized in weather topics with streaming support.
    """
    try:
        tools = [
            calculator,
            think,
            current_time,
            AgentCoreCodeInterpreter(region=AWS_REGION).code_interpreter
        ]
        # Domain-specific tools
        tools += WeatherTools(latitude=MY_LATITUDE, longitude=MY_LONGITUDE).get_tools()

        research_agent = get_agent(
            system_prompt=WEATHER_ASSISTANT_PROMPT,
            tools=tools
        )

        async for token in research_agent.stream_async(query):
            yield token

    except Exception as e:
        yield f"Error in research assistant: {str(e)}"

Each agent has access to domain-specific tools. For example, the weather agent uses external APIs:

class WeatherTools:
    def __init__(self, latitude: float, longitude: float):
        self.latitude = latitude
        self.longitude = longitude

    def get_tools(self) -&gt; List[tool]:
        @tool
        def get_hourly_weather_data(from_date: date, to_date: date) -&gt; MeteoData:
            """Get hourly weather data for a specific date range."""
            url = (f"https://api.open-meteo.com/v1/forecast?"
                   f"latitude={self.latitude}&amp;longitude={self.longitude}&amp;"
                   f"hourly=temperature_2m,relative_humidity_2m...")
            response = requests.get(url)
            return parse_weather_response(response.json())
        
        return [get_hourly_weather_data]

The logistics and production agents use synthetic data generators for demonstration:

class LogisticsTools:
    def get_tools(self) -&gt; List[tool]:
        @tool
        def get_logistics_data(
            from_date: date,
            to_date: date,
            origins: Optional[List[str]] = None,
            destinations: Optional[List[str]] = None,
        ) -&gt; LogisticsDataset:
            """Generate synthetic logistics shipment data."""
            # Generate realistic shipment data with delays, costs, routes
            records = generate_synthetic_shipments(...)
            return LogisticsDataset(records=records, aggregates=...)
        
        return [get_logistics_data]

For UI we’re going to use Chainlit. The Chainlit integration provides real-time visibility into agent execution:

class LoggingHooks(HookProvider):
    async def before_tool(self, event: BeforeToolCallEvent) -> None:
        step = cl.Step(name=f"{event.tool_use['name']}", type="tool")
        await step.send()
        cl.user_session.set(f"step_{event.tool_use['name']}", step)

    async def after_tool(self, event: AfterToolCallEvent) -> None:
        step = cl.user_session.get(f"step_{event.tool_use['name']}")
        if step:
            await step.update()

@cl.on_message
async def handle_message(message: cl.Message):
    agent = cl.user_session.get("agent")
    message_history = cl.user_session.get("message_history")
    message_history.append({"role": "user", "content": message.content})
    
    response = await agent.run_async(message.content)
    await cl.Message(content=response).send()

This creates a transparent experience where users see:

  • Which agent is handling their request
  • What tools are being invoked
  • Real-time streaming of responses

Now we can handle a variety of user queries: For example:

User: “What was the average temperature last week?”

Flow:

  1. Orchestrator identifies weather domain
  2. Routes to weather_assistant
  3. Weather agent calls get_hourly_weather_data
  4. Analyzes and returns formatted response

Or multi-domain queries:

User: “Did weather conditions affect our shipment delays yesterday?”

Flow:

  1. Orchestrator identifies weather + logistics domains
  2. Routes to weather_assistant for climate data
  3. Routes to logistics_assistant for shipment data
  4. Synthesizes correlation analysis
  5. Returns unified insight

And complex analytics:

User: “Analyze production efficiency trends and correlate with weather and logistics performance based in yesterday’s data.”

Flow:

  1. Orchestrator coordinates all three agents
  2. Production agent retrieves manufacturing KPIs
  3. Weather agent provides environmental data
  4. Logistics agent supplies delivery metrics
  5. Orchestrator synthesizes multi-domain analysis

This architecture scales naturally in multiple dimensions. We can easily add new specialized agents without disrupting existing functionality. WE only need to create the new agent and register it as a tool with the orchestratortrator prompt with new domain description. That’s it.

The orchestrator pattern transforms multi-domain AI from a monolithic challenge into a composable architecture. Each agent focuses on what it does best, while the orchestrator provides intelligent coordination.

Full code in my github.