LLM Observability
vs
AI Agent Governance
Observability watches your agents fail. Governance prevents the failure. They serve different purposes, operate at different layers, and solve different problems. You need both. But only one can stop a dangerous action before it executes.
The Core Distinction
LLM Observability
- → Logs prompts and completions
- → Traces LLM API calls
- → Counts tokens and costs after execution
- → Surfaces failures after they happen
- → Operates at the model layer
AI Agent Governance
- → Evaluates agent actions before execution
- → Blocks dangerous actions deterministically
- → Enforces policies per agent in real-time
- → Prevents failures before they happen
- → Operates at the action layer
Feature Comparison
| Capability | Observability | Governance |
|---|---|---|
| Action blocking | No — logs after execution | Yes — DENY before execution |
| Kill switch | No — no runtime control | Yes — sub-8ms halt |
| Policy enforcement | No — no policy engine | Yes — deterministic rules per agent |
| Prompt/completion logging | Yes — primary function | Optional — not the focus |
| Tool call interception | Logs tool calls post-execution | Evaluates + blocks before execution |
| PII detection | Some — detection only | Yes — detection + redaction + blocking |
| Tamper-proof audit | No — mutable logs | Yes — cryptographically chained, immutable |
| Compliance evidence | No — not structured for compliance | Yes — EU AI Act, SOC 2, NIST AI RMF |
| Behavioral profiling | Trace visualization | Behavioral analysis + anomaly detection |
| Agent state management | No — agents are stateless to the tool | Yes — progressive trust levels |
| Cost controls | Token counting after the fact | Real-time metering + circuit breakers |
| Multi-agent coordination | Per-trace, per-agent | Cross-agent policies + delegation governance |
Where Each Tool Fits
These are good tools. They solve observability. They don't solve governance.
LangSmith
LangChain ecosystem tracing, prompt engineering, evaluation
No action blocking, no kill switch, no compliance evidence. Logs actions — does not prevent them.
Langfuse
Open-source LLM observability, cost tracking, prompt management
No policy enforcement, no runtime control. Provides dashboards, not governance.
Helicone
LLM API proxy for logging, caching, cost tracking
Operates at the LLM API layer. Does not see or govern agent actions (file writes, tool calls, etc.).
Datadog LLM Monitoring
Infrastructure-grade LLM monitoring, APM integration
Monitors model performance and infrastructure. Does not govern autonomous agent actions.
Arize Phoenix
LLM evaluation, embedding analysis, trace visualization
Evaluation and debugging tool. No runtime governance, no real-time blocking.
Use Both. For Different Purposes.
Observability and governance are complementary, not competing. Use LangSmith to debug your chains. Use Langfuse to track prompt iterations. Use Datadog to monitor infrastructure.
Use OnLeash to stop dangerous actions before they execute, enforce deterministic policies on every agent, generate compliance evidence, and maintain kill switch capability across your entire agent fleet.
OnLeash is not a replacement for observability. It's the governance layer that observability tools cannot provide. The control plane sits alongside the monitoring plane.
Add Governance to Your Observability Stack
Works alongside LangSmith, Langfuse, Helicone, and Datadog. Free Developer tier.
Deploy Governance Layer