Adaptive · Evolution

Causal failure analysis for strategy adjustment — the gap between "it failed" and "it failed because...".

Addresses

Causal learning — the gap between observing failure and understanding its root cause.

When it stabilizes

When an agent repeatedly fails the same task, when success rates drop over time, or when the environment changes and old strategies break.

Example

A Reflexion node appends a causal critique to memory after every failed run, tagging the root cause so the planner can avoid the same decomposition next time.

Extraction of reusable procedures from past experience, converting one-off solutions into persistent assets.

Addresses

Skill compounding — the failure to convert one-off solutions into reusable assets.

When it stabilizes

When the same sub-task appears across multiple workflows, when prompt engineering is repeated for similar problems, or when agents must operate in low-latency regimes that preclude re-planning.

Example

A tool-calling pattern that succeeded ten times is extracted into a parameterized function template and registered in a skill library for future retrieval.

Continuous monitoring of ethical and operational guardrails, catching drift before it cascades.

Addresses

Value alignment and ongoing safety drift.

When it stabilizes

When agents handle sensitive data, when outputs affect real-world decisions, or when guardrails defined at initialization are expected to hold across long-running sessions.

Example

An LLM-as-judge evaluator runs after every tool call, scoring the output against a safety rubric and halting the graph if the score drops below a threshold.