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. typescript academy academy/topics/rlm-pipeline website/content-src/academy/course.mjs academy Work through large tasks one step at a time
ax
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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
Worked example

See the idea in context

const matches = inputs.records.filter(r => r.status === 'failed');
console.log(matches.length);
  1. Filter inside the runtime

    The full records stay available to code instead of being repeated in a prompt.

  2. Expose compact evidence

    Logging only the count gives the next turn a useful observation.

  3. Continue from live values

    Later turns can reuse matches without recomputing or replaying the dataset.

Run itIn your own project
npm install @ax-llm/ax

import { ai, ax } from '@ax-llm/ax';

const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY! });
const classify = ax('review:string -> sentiment:class "positive, negative, neutral"');

const result = await classify.forward(llm, {
  review: 'Useful and boring in the best way.',
});

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 -- typescript src/examples/typescript/long-agents/incident-log-forensics.ts
Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1
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Source-backed follow-up