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Agent Sentinel helps you observe, control cost, and enforce safety policies across AI agent runs—without breaking your agent when telemetry fails.

What you get

Python SDK

  • Instrumentation: @guarded_action decorator for automatic telemetry
  • Policy enforcement: Budgets, allow/deny lists, rate limits
  • Human-in-the-loop: Approval workflows for sensitive actions
  • Intervention tracking: See what Sentinel blocks and why
  • Remote sync: Background sync to platform
  • Replay mode: Zero-cost deterministic replay for debugging
  • LLM integrations: Transparent instrumentation for OpenAI, Anthropic, Grok, Gemini
  • Framework support: Native LangChain and CrewAI integrations
  • MCP client: Model Context Protocol for LLM-platform interaction
  • Error handling: Comprehensive error types with recovery strategies

Platform

  • Agent discovery: Automatic registration and metrics
  • Interventions: Track policy enforcement and cost prevention
  • Approvals: Human-in-the-loop approval workflows
  • Activity Ledger: Complete audit trail with advanced filtering
  • WebSocket real-time: Live event streaming
  • MCP endpoints: LLM-native API for autonomous operations
  • Stats & analytics: Cost tracking and performance metrics
  • Replay analysis: Determinism scoring and divergence detection
  • Compliance: EU AI Act Article 14 support

Web Console

  • Real-time dashboard: Live monitoring with WebSocket updates
  • Interventions page: Policy enforcement visibility
  • Approvals inbox: Review and approve sensitive actions
  • Runs explorer: Execution history with replay simulation
  • Activity Ledger: Complete audit trail with export
  • Agent management: Fleet overview with performance metrics
  • Analytics: Cost dashboards and optimization recommendations
  • Settings: Policy builder, API key management, notifications

Quickstart

Get started in 10 minutes - instrument your agent and sync to the platform.

Python SDK

Full SDK documentation - instrumentation, policies, approvals, LLM integrations.

Platform API

Platform documentation - agents, interventions, approvals, real-time updates.

Web Console

Web UI guide - dashboard, interventions, approvals, runs, analytics.

How Agent Sentinel fits into your agent

At runtime your agent code calls tools / functions. Agent Sentinel wraps those calls:
  • Before execution: policy engine checks allow/deny lists, budgets, and rate limits.
  • After execution: a ledger entry is appended (inputs, outputs, cost, duration, outcome).
  • Optional: a background sync thread batches ledger entries to the platform (/api/v1/ingest/).
┌────────────────────────────┐
│ Your agent code            │
│  @guarded_action(...)      │
└──────────────┬─────────────┘
               │ (policy check + timing)

┌────────────────────────────┐
│ Local ledger (.jsonl)      │
│  .agent-sentinel/ledger…   │
└──────────────┬─────────────┘
               │ (optional background sync)

┌────────────────────────────┐
│ Platform                   │
│  POST /api/v1/ingest/      │
└──────────────┬─────────────┘

┌────────────────────────────┐
│ Postgres (runs + actions)  │
└────────────────────────────┘

Key features

Interventions

Track what Agent Sentinel blocks and demonstrate platform value.

Approvals

Human-in-the-loop approval workflows for sensitive actions.

LLM integrations

Transparent instrumentation for OpenAI, Anthropic, Grok, Gemini.

Agent discovery

Automatic agent registration with performance metrics.

Real-time updates

WebSocket event streaming for live dashboard updates.

Web console

Modern web UI for monitoring, approvals, and analytics.

Core concepts

Actions & runs

The core data model: runs contain actions; actions have cost and outcome.

Policies

Budgets, allow/deny lists, rate limits, and remote policy sync.

Activity Ledger

Complete audit trail with compliance metadata and export.
Agent Sentinel is fail-open for telemetry (ledger writes and sync should never crash your agent), but policy enforcement is fail-closed by design (violations raise).