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ATLAS

A failure-mode taxonomy layer for agents: reflect at meaningful boundaries, catch recurring mistakes, and learn from traces.

ATLAS runtime loop

ATLAS gives an agent a structured way to notice recurring mistakes at runtime, record evidence, and generate or refine task-specific failure-mode taxonomies from completed traces.

Runs start from MAST — the Multi-Agent System failure Taxonomy from "Why Do Multi-Agent LLM Systems Fail?" (Cemri et al., 2025), shipped as a built-in 14-code adaptation. At configured gates the agent reflects on its recent trajectory (Observe → Correlate → Map → Decide), a blocking final gate runs before submission, and completed traces feed taxonomy generation and refinement. ATLAS is not a task solver; your harness keeps owning model execution.

Quickstart

Install:

python -m pip install "git+https://github.com/multi-agent-systems-failure-taxonomy/ATLAS.git"

Create atlas.json in the project that will run the agent:

{
  "version": 1,
  "trace_output": "./atlas-program",
  "atlas_model": "gpt-5"
}

Then choose the integration that matches your pipeline:

Use case Command Full docs
Claude Code project atlas-claude-install --project-dir . --config atlas.json Claude Code
Codex project atlas-codex-install --project-dir . --config atlas.json Codex
One LLM call from a script atlas-single-run --config atlas.json --task "..." --model gpt-5 Single LLM
Existing trace folder atlas-import-traces --config atlas.json --traces ./traces Taxonomies
Your own harness from atlas_runtime import start_session, ... Pipeline integration

Check the setup:

atlas-doctor --config atlas.json

Where to start