behavioral-state-decay

field reference

Long-running agents drift.
This site documents how, how much, and what helps.

behavioral_state_decay(t) — observed loss of instruction adherence, calibration, and consistency as agent sessions grow long.

Documenting, measuring, and mitigating behavioral degradation in long-running AI agents — definitions, metrics, and mitigation patterns from real operations.

latest entries

full log

Definitions

7 min

Tool Output Truncation: When an Agent Reasons Over a Result It Never Fully Saw

Tool output truncation is the failure mode where a tool call succeeds but the result handed back to the agent has already been cut down to a size cap, with no signal in the payload that anything is missing. Grounded in published framework defaults (Codex's 10 KiB/256-line cap, Claude Code's 25,000-token default) and a 2026 audit documenting 91 silent failures in a real tool-calling pipeline, this entry covers the mechanism, why it fails silently rather than loudly, and the structural fix: completeness flags, not longer logs.

Bridges

8 min

What Research Leaders Should Look For in Calibration, Confidence Scores, and Model Drift When Evaluating Customer Simulation Software

Customer simulation software generates synthetic respondents, personas, or 'digital twins' with an LLM underneath, which means it inherits agent reliability's hardest problem: confidence numbers and model behavior that can drift without any visible error. A concrete evaluation framework for research leaders: what calibration evidence to demand before a pilot, what confidence-score behavior is a red flag, and how to test for model drift before a research program depends on a vendor's output.

Definitions

6 min

What Is Context Decay? Definition, Mechanism, and How It Happens

Context decay is the informal, umbrella name people reach for when an AI agent's grip on its own context window loosens over a session. This entry defines it precisely, breaks down the two distinct mechanisms it usually points at (context rot and context drift), and walks through how the decay actually happens step by step.

Bridges

6 min

Calibration Drift Meaning: The ML Monitoring Term vs. the AI Agent Failure Mode

Calibration drift means two different things depending on which field you found it in: in MLOps monitoring it is a deployed classifier's predicted probabilities no longer matching true outcome frequencies after a data shift, typically fixed by recalibration or retraining; in AI agent research it is an agent's self-reported confidence separating from its own accuracy within a single running session, with no data shift or retraining involved at all. This entry disambiguates both senses, with citations, and routes to this site's full mechanism breakdown of the agent-session version.

Definitions

7 min

Consensus Collapse: When Multi-Agent LLM Debate Discards the Right Answer to Agree

Consensus collapse is the specific failure mode where a multi-agent LLM debate's final plurality vote discards a correct answer that was already present among the agents' own generations, one of three named failure modes in a 2026 controlled study that found homogeneous debate loses to isolated self-correction on both accuracy and cost. How it differs from sycophantic drift and hallucination cascades, what the numbers say, and when debate is still worth running.

Mitigation

7 min

Drift-Aware Routing: Resetting or Rerouting an Agent Before It Fails, Not After

Drift-aware routing feeds a running agent's own stability score back into the dispatcher: delegation prefers stable agents, and a drifting one gets reset rather than left to keep working. How it differs from the intent-based handoffs production frameworks ship today, what a 2026 simulation measured for the strategy on its own, and how to build a stability-gated router without the risks a bare context wipe introduces.