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. go academy academy/topics/rlm-pipeline website/content-src/academy/course.mjs academy Work through large tasks one step at a time
ax
Go github.com/ax-llm/ax/packages/go
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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
Unit example (nearest native match)

See the idea in context

investigator := axllm.NewAgent(signature, map[string]axllm.Value{
    "contextFields": axllm.Array("logs"),
    "contextPolicy": axllm.Object("preset", "lean", "budget", "balanced"),
    "runtime": axllm.Object("language", "JavaScript"),
})
  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
go get github.com/ax-llm/ax/packages/go

import axllm "github.com/ax-llm/ax/packages/go"

client := axllm.NewAI("openai", map[string]axllm.Value{"apiKey": os.Getenv("OPENAI_API_KEY")})
classify := axllm.NewAx("review:string -> sentiment:class \"positive, negative, neutral\"", nil)

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 -- go src/examples/go/long-agents/incident_log_forensics.go
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