Unit 6 · Solve long and complex tasks

Work through large tasks one step at a time

You let the actor run one observable step, inspect compact evidence, and continue from live state. This avoids stuffing a large task into one prompt or generated script.

agent()9 focused minutesNot started

Unit example (nearest native match)

See the idea in context

let investigator = agent_with_options(signature, json!({
    "contextFields": ["logs"],
    "contextPolicy": {"preset": "lean", "budget": "balanced"},
    "runtime": {"language": "JavaScript"},
}))?.with_runtime(Box::new(QuickJsCodeRuntime::new()))?;

Filter inside the runtime
The full records stay available to code instead of being repeated in a prompt.
Expose compact evidence
Logging only the count gives the next turn a useful observation.
Continue from live values
Later turns can reuse matches without recomputing or replaying the dataset.

Run itIn your own project

cargo add axllm

use axllm::{ai, ax, AxResult};
use serde_json::json;

fn main() -> AxResult<()> {
    let mut llm = ai("openai", json!({"apiKey": std::env::var("OPENAI_API_KEY")?}))?;
    let mut classify = ax("review:string -> sentiment:class \"positive, negative, neutral\"")?;
    let result = classify.forward(&mut llm, json!({
        "review": "Useful and boring in the best way."
    }))?;

    println!("sentiment: {}", result["sentiment"].as_str().unwrap_or_default());
    Ok(())
}

Set OPENAI_APIKEY in your environment before running provider-backed code.

In the ax repo

From a clone of the ax repo:

npm run example -- rust src/examples/rust/long-agents/incident_log_forensics.rs

Active practice

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Answer 2 in a row to learn this · attempt 1

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Source-backed follow-up