How AI Agents Monitor Their Own Runtime Behavior

When an AI agent modifies your API handler, updates a database migration, or changes a configuration file, it has no way to verify whether the change worked correctly at runtime. Unit tests validate code correctness. Integration tests validate contract compliance. But neither tells you whether system behavior changed in ways that matter.

The gap between "the code compiles and tests pass" and "the system behaves correctly in production" is where regressions hide. For human developers, this gap is bridged by experience, intuition, and manual testing. Agents have none of these.

Runtime intelligence closes this gap by capturing actual system behavior, comparing it against a known-good baseline, and producing a structured verdict: what changed, whether it matters, and what to do next.

Logito exposes runtime intelligence through the Model Context Protocol (MCP), the standard interface for AI agents to interact with external tools. Any MCP-compatible agent (Claude, Codex, custom agents) can use these tools.

Key MCP tools for agents

# Get the latest run summary
logito.get_latest_run

# Review a specific run for drift
review_run { "run_id": "..." }

# Get structured diff against baseline
logito.get_run_diff { "run_id": "..." }

# Check system status
logito.get_system_status

# Investigate a specific issue
logito.explain_issue { "issue_id": "..." }

# Get recommended next action
logito.get_recommended_action { "run_id": "..." }

Agent workflow example

# 1. Agent starts a local capture session
logito dev start --project my-api --local

# 2. Agent runs the test suite
npm test

# 3. Agent reviews the run
logito review --json

# 4. Agent checks for regressions
# If review shows regressions, agent investigates:
logito ask "what caused the latency regression on /checkout?"

# 5. Agent compares against previous run
logito compare last

At the core of Logito's intelligence is a causal graph — a directed graph of service dependencies, deployment events, and observed runtime relationships. Unlike static service maps, the causal graph is built from actual runtime observations and updated continuously.

What the causal graph captures

• Service call patterns: Which services call which, how often, with what latency
• Deployment events: When each service was deployed, what changed
• Error cascades: When an error in service A causes failures in services B, C, D
• Propagation timing: How long it takes for a change to propagate through dependent services

What it enables

• Impact prediction: "If service-A is deployed, services B and C will likely be affected within 5 minutes"
• Root cause inference: "The latency spike in service-C was likely caused by the deployment to service-A 12 minutes earlier"
• Pattern recognition: "This failure pattern has occurred 3 times before and was resolved by rolling back the cache configuration"
• Confidence calibration: Actions that historically succeed get higher confidence scores, creating a self-improving system

How confidence calibration works

Every action Logito recommends is tracked through execution. When an action improves the situation, the confidence for that action type increases. When it doesn't help, confidence decreases. Over time, the system learns which remediation patterns work for your specific infrastructure — institutional memory that persists across team members.

This is the capability that no observability tool currently offers: not just detecting what changed, but predicting what will happen next and calibrating that prediction against reality.