Agentic Self-Instruct
Agentic Self-Instruct: weak-vs-strong synthetic data
Agentic Self-Instruct is a four-role loop for synthetic dataset generation: challenger proposes, weak and strong solvers attempt, judge filters. DataSmith ships it as a Python SDK; AgentClash runs it hosted for eval-ready datasets.
Candidate
Baseline
Control
replay timeline
ci verdict
Correctness improved, latency within budget, and required artifacts were preserved for review.
agentclash run create --follow
The four roles
Built for reviewable agent decisions
Challenger generates candidates from seeds. Weak solver represents the model you want to improve. Strong solver represents a reference path. Judge scores quality and separation before acceptance.
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
Workflow
Research to production
Import the evidence
Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.
Pin the baseline
Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.
Replay the evidence
Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.
Gate the release
Compare candidate and baseline runs, then fail CI before a regression reaches users.
Implement the loop
Bring your first workload into the loop
Read the synthetic generation guide, then try DataSmith locally or Agentic Self-Instruct in a workspace dataset.
Agent evals
Real-task agent evals with replay evidence and CI gates.
LLM agent evaluation
Evaluate LLM agents on full trajectories, not one-shot answers.
Compare tools
See how AgentClash differs from prompt-eval platforms.
DataSmith GitHub
Open-source Python SDK and CLI.
Synthetic data generation
Broader SEO hub for agent dataset generation.
Glossary: Agentic Self-Instruct
Short definition and when to use it.
Datasets overview
Import examples, record baselines, sync regression suites, and gate CI.
FAQ
Agentic Self-Instruct FAQ
What is Agentic Self-Instruct?
An agentic loop where a challenger generates examples and weak/strong solvers plus a judge determine whether each row is in the useful difficulty zone for training.
How is it related to Meta FAIR Autodata?
Autodata formalized weak-vs-strong agentic self-instruct for synthetic data. DataSmith implements the practical inner loop; it is not an official Meta release.
Can I tune acceptance thresholds?
Yes. Both DataSmith and AgentClash expose strong pass rate, weak fail rate, and minimum score gap controls.