# What is Agentic Self-Instruct?

Agentic Self-Instruct generates synthetic training examples by proposing tasks, running weak and strong solvers, and accepting rows only when the strong path succeeds and the weak path struggles.

Source: https://www.agentclash.dev/glossary/agentic-self-instruct
Markdown export: https://www.agentclash.dev/md/glossary/agentic-self-instruct

## How it differs from self-instruct

Classic self-instruct prompts a model for more examples. Agentic Self-Instruct adds solver rollouts and a judge so difficulty is measured, not assumed.

- OpenTelemetry-compatible trace import
- Pinned datasets and golden test cases
- Baseline versus candidate regression checks
- Replay trails for tool calls, outputs, and artifacts
- Scorecards for correctness, cost, latency, and evidence
- CI gates for prompt, model, RAG, and tool changes

## Typical roles

### 1. Import the evidence

Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.

### 2. Pin the baseline

Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.

### 3. Replay the evidence

Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.

### 4. Gate the release

Compare candidate and baseline runs, then fail CI before a regression reaches users.

## Go deeper

Read the Agentic Self-Instruct landing page and synthetic generation docs, or install DataSmith for local runs.

- [Agent evals](https://www.agentclash.dev/agent-evals): Real-task agent evals with replay evidence and CI gates.
- [LLM agent evaluation](https://www.agentclash.dev/llm-agent-evaluation): Evaluate LLM agents on full trajectories, not one-shot answers.
- [Compare tools](https://www.agentclash.dev/compare): See how AgentClash differs from prompt-eval platforms.
- [Agentic Self-Instruct hub](https://www.agentclash.dev/agentic-self-instruct): SEO landing with workflow and FAQ.
- [DataSmith platform](https://www.agentclash.dev/platform/datasmith): Open-source SDK overview.
- [Glossary index](https://www.agentclash.dev/glossary): More AgentClash terms.
- [Datasets overview](https://www.agentclash.dev/docs/guides/datasets-overview): Import examples, record baselines, sync regression suites, and gate CI.

## Agentic Self-Instruct FAQ

### Who popularized Agentic Self-Instruct?

Meta FAIR's Autodata paper formalized weak-vs-strong agentic self-instruct for synthetic data generation and meta-optimization.

### What is the useful difficulty zone?

Examples where a strong solver passes and a weak solver fails, indicating the row can teach the weak model something new.

### Where can I run it?

DataSmith (pip install datasmith) for local export, or AgentClash workspaces for hosted generation tied to eval gates.