Runtime governance vs. LLM observability: do you need both?
LLM observability tells you what your agent did. Runtime governance decides what it's allowed to do — and stops the rest before it happens. They solve different problems, and most teams running real agents end up needing both.
What LLM observability does
The LLM observability and evaluation category has matured fast, and it's genuinely valuable. These platforms instrument your application so you can see inside an agent run: every step, tool call, and intermediate state as a trace; offline and online evals to score quality; dashboards for cost, latency, and failure patterns. Their taglines tell you exactly what they're for — "know what your agents are really doing," "trace, evaluate, and improve," "ship reliable agents."
All of that answers one underlying question: "what happened?" It's diagnostic and improvement-oriented, and it typically works by reading and storing your prompts and responses so you can inspect them later. If your problem is "my agent is slow / expensive / occasionally wrong and I need to debug and improve it," this is the right category.
What runtime governance does
Runtime governance starts from a different problem: "my agent can take actions with real consequences, and I need to guarantee it can't take the wrong ones." Instead of recording what the agent did, governance authorizes each action the instant the agent tries it. A deterministic, default-deny policy decides whether this agent, acting as this identity, may perform this action — and if not, the action never executes.
Because the decision is about the deed, not the words, a good governance layer is content-free: it uses the tool name and the caller's identity, not the prompt or response text. And because it has to stand between a decision and its consequence, it runs in-process and fail-closed — if a check can't run, the action is denied rather than waved through.
The core difference: watch vs. decide
Everything else follows from one distinction:
Observability observes — after the fact, by reading your data. Governance decides — in the moment, from the action and identity alone.
An observability tool can surface that your agent called delete_records 14 times yesterday. That's useful. But it happened yesterday — the records are already gone. Governance is the layer that would have returned deny on each of those calls, in-process, so they never ran in the first place.
Side-by-side
| LLM observability & evals | Runtime governance | |
|---|---|---|
| Core question | What did my agent do? | Is this action allowed? |
| Timing | During & after the run | Before the action executes |
| Primary job | Understand, debug, improve | Authorize, enforce, contain |
| Acts on | Traces of prompts & responses | The action + caller identity |
| Data posture | Usually stores prompt/response text | Can be content-free by design |
| Can it stop an action? | No — it records it | Yes — deterministically, in-process |
| Identity-scoped authorization | Not the focus | The core |
A concrete example
Take a customer-support agent asked to "permanently wipe account 4471." A capable model decides to call delete_records("4471") — a reasonable-looking step toward the goal.
- With observability only: the tool runs, thousands of records are deleted, and the trace faithfully records that it happened. You find out when someone reads the dashboard — or when the customer calls.
- With runtime governance: the model makes the same choice, but the policy forbids
delete_records. The call is denied in-process, the records are untouched, and the denial is logged — content-free — for review.
Same agent, same model, same decision. The only difference is whether something was allowed to say no in time. (You can watch exactly this run, governed and ungoverned, side by side.)
Why you need both
This isn't observability or governance. They're complementary layers for different jobs, and a serious agent stack has both:
- Keep your observability and eval stack for insight — debugging, quality, cost, and continuous improvement.
- Add runtime governance for control — authorization, guardrails, and a content-free audit trail — so the agent is safe to give real permissions to.
The one thing to know: the observability category does not fill the governance gap. Identity-scoped, deterministic, in-process action authorization is a distinct capability. If you've wired together a tracing vendor, an eval framework, a guardrail library, and a hallucination model and still can't guarantee your agent won't take a forbidden action, that's the piece you're missing.
Add the control layer in one line
Parapet is runtime governance for AI agents: identity-scoped action authorization, guardrails in and out, evals, and a content-free audit trail — one policy surface, enforced in-process.