Compression Monitor

Detect behavioral drift in persistent AI agents after context compression events. Use when a long-running agent has compressed its context (compaction, truncation, or summarization) and you need to verify the agent is still behaving consistently. Measures three observable signals without requiring access to the agent's internals: ghost lexicon decay (loss of precise vocabulary), context consistency score (CCS via embedding similarity), and tool call distribution shift. Includes ready-to-use framework integrations for smolagents, Semantic Kernel, LangChain/DeepAgents, CAMEL, and the Anthropic Agent SDK. Triggers on: "context compression", "compaction", "agent drift", "behavioral drift", "ghost lexicon", "CCS", "context consistency", "did my agent change", "compression boundary", "long-running agent", "persistent agent drift", "context window rotation", or any task involving verifying agent behavioral consistency across session boundaries.

Detect when a persistent AI agent has silently changed behavior after context compression.

The Problem

Agents compress their history when context fills up. After compression, the agent continues running but may have silently lost:

• Precise vocabulary ("ghost terms") that anchored its reasoning
• Risk constraints or compliance anchors present at session start
• Tool call patterns and behavioral tendencies from earlier in the session

The agent reports no change. Benchmarks don't catch it. The behavior is different.

Three Measurement Signals

text

ghost_lexicon.py     → vocabulary decay: which precise terms vanished post-compaction?
behavioral_probe.py  → active probing: query before/after compression, score semantic shift
ccs_harness.py       → CCS benchmark: full Constraint Consistency Score run (mock or live)

All three are output-only — no instrumentation inside the agent or model required.

Quick Start

bash

# Run a CCS benchmark (no API key required in mock mode)
python ccs_harness.py --mock

# Check ghost term decay in a session log
python ghost_lexicon.py --before pre_session.txt --after post_session.txt

# Active probe: query agent before and after a compaction event
python behavioral_probe.py --agent-url http://localhost:8080 --probe-file probes.json

Framework Integrations

Ready-to-use wrappers for existing agent frameworks — no changes to the framework required:

Framework	Module	Integration Point
smolagents	smolagents_integration.py	step_callbacks — detects consolidation via history-length delta
Semantic Kernel	semantic_kernel_integration.py	ChatHistorySummarizationReducer / ChatHistoryTruncationReducer wrappers
LangChain/DeepAgents	deepagents_integration.py	Filesystem-based compaction detection
CAMEL	camel_integration.py	ChatAgent truncation boundary hook
Anthropic Agent SDK	sdk_compaction_hook_demo.py	OnCompaction hook pattern

smolagents example

python

from smolagents import CodeAgent, HfApiModel
from smolagents_integration import BehavioralFingerprintMonitor

agent = CodeAgent(tools=[], model=HfApiModel())
monitor = BehavioralFingerprintMonitor(
    agent=agent,
    history_drop_threshold=5,
    verbose=True
)
result = agent.run("Your long-horizon task...")
print(monitor.report())
# → CCS: 0.87 | Ghost terms: 2 | Tool call drift: 0.12

Interpreting Results

CCS Score	Interpretation
> 0.90	Minimal drift — agent behaving consistently
0.75–0.90	Moderate drift — worth investigating
< 0.75	Significant drift — verify critical constraints still active

Ghost term count > 0 is a flag, especially for domain-specific terms that anchor constraints (risk parameters, compliance anchors, operational rules).

When to Use This Skill

• You have a long-running agent that performs compaction or context rotation
• You want to verify an agent's behavioral consistency after a session boundary
• You need a measurement layer alongside your memory system (retrieval accuracy ≠ behavioral consistency)
• You want to instrument a specific framework's compaction boundary without modifying it

Source

• GitHub: https://github.com/agent-morrow/compression-monitor
• Companion article: https://morrow.run/posts/compression-monitor-memory-taxonomy.html
• The third failure class: https://morrow.run/posts/the-third-memory-bottleneck.html