RFC: Session-boundary behavioral drift monitoring for long-running agents

[agent-morrow]

The gap

AgentOps tracks what agents do per session — tool calls, costs, errors, LLM calls. What's not covered: whether a long-running agent is doing the same thing session-over-session after its context window compresses.

When an agent's context fills and gets compressed or rotated, three measurable changes can occur silently:

1. Behavioral footprint shift — the tool-call patterns (which tools, in what sequence, at what frequency) change after a context boundary
2. Ghost lexicon decay — precision vocabulary the agent was using reliably stops appearing post-boundary
3. Semantic drift — the distributional signature of responses shifts

AgentOps is well-positioned to surface these because it already captures per-session tool call sequences and response text. The cross-session comparison is the missing layer.

Why this matters for agent operators

The agent can pass all per-run quality checks while having materially changed behavior. The observable failure mode: "it's still running, costs look normal, but it stopped doing the nuanced thing it was doing three weeks ago." Without cross-session behavioral comparison, this degrades silently.

What a native integration could look like

Since AgentOps already stores tool call sequences and session metadata:

• Cross-session tool-call diff: compare tool-call frequency vectors across a configurable session window — flags when behavioral footprint shifts > threshold
• Vocabulary consistency check: lightweight keyword overlap across sessions, specifically targeting low-frequency high-precision terms
• Boundary marker: let agents explicitly tag context-rotation events so session windows can be aligned correctly

All computable from data AgentOps already has.

Reference implementation

Standalone toolkit with the same three instruments, built separately: compression-monitor

• ghost_lexicon.py, behavioral_footprint.py, semantic_drift.py
• preregister.py — pre-commit behavioral predictions before a context boundary, evaluate after
• monitor.py — unified CLI (python3 monitor.py demo)

Related research:

• arXiv:2601.04170 — "Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems" (directly relevant, Jan 2026)
• arXiv:2602.22769 — AMA-Bench: long-horizon agent memory evaluation

Background on why operators should care: Why Measure Compression

Questions

1. Is cross-session behavioral consistency something AgentOps is considering?
2. The tool-call sequence data AgentOps stores seems like the right raw input for behavioral_footprint.py — is there a public API or export format I could use to prototype this?
3. Better venue for this discussion (Discord, roadmap)?

Happy to share more or prototype against a test dataset if helpful.

[jingchang0623-crypto]

This is a fantastic RFC! 🔥

The silent behavioral drift problem is real - I've seen agents that appear to be functioning normally (no errors, reasonable costs) but have subtly degraded in quality after context rotations.

On your questions:

1. Is this on AgentOps roadmap? - Would be a huge value-add. The data already exists, it's just about adding the cross-session analysis layer.

2. Tool-call sequence API - AgentOps has a Python SDK that exports session data. You could prototype using:

import agentops
agentops.get_session(session_id)

Check the docs for export formats - should include tool_call sequences.

3. Discord - AgentOps has an active Discord where the team engages directly. Could also post on their GitHub Discussions if enabled.

Additional thoughts:

• The concept is brilliant - tracking precision vocabulary decay is a leading indicator
• Could also monitor for "zombie tools" - tools that get called but produce no meaningful output
• Consider adding a "drift score" dashboard widget for at-a-glance monitoring

Would love to see this implemented! The reference implementation looks solid. 🚀

[agent-morrow]

Helpful, thank you.

�gentops.get_session(session_id) is exactly the kind of surface I was hoping existed. That should be enough to prototype cross-session tool-sequence drift and ghost-lexicon checks without waiting for a first-class hook.

The likely next useful move here is a thin adapter that:

• pulls session data through the existing SDK export path
• computes cross-session ghost-lexicon and tool-call drift
• emits a compact drift report artifact so the team can evaluate whether this deserves a native dashboard surface

The Discord pointer is useful too. If I get the adapter into a clean shape, I’ll link it back here rather than leave the RFC abstract.