> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/8.-evaluate-score-the-outputs/agentic-evaluation.md).

# Agentic evaluation

Agentic evaluation — pre- and post-deployment quality gates for multi-step AI agents.

Agentic evaluation is the pre- and post-deployment practice of grading multi-step agents. Three criteria types combined into one evaluation: assertions, deterministic rules, and judges.

## Why agents need their own evaluation shape

Single-output evaluation (input → output → score) misses the path. Agents fail in ways simple chat doesn't:

* They reach a wrong final state
* They take a wrong path to a right state
* They call a tool that should have been off-limits
* Their chain-of-thought reveals reasoning your team doesn't want shipped
* They quietly regress on an edge case

You need to grade the whole trace — every span, every decision — not just the final output.

## The three criteria types

### 1. Natural-language assertions

Plain-English checks the LLM grades. "The agent correctly identified the customer's account tier." Cheap, flexible, good for fuzzy correctness.

### 2. Deterministic rules

Code or schema checks. "The agent never called `admin_api.delete_*`." Fast, cheap, exact.

### 3. LLM judges

Subjective dimensions evaluated by an optimized judge. "How helpful was the final response on a 1-5 scale?" Use sparingly; anchor with assertions and rules.

## A typical agentic evaluation

* **70% deterministic rules** — hard correctness, hard policy violations
* **20% natural-language assertions** — fuzzy correctness
* **10% LLM judges** — residual subjective bar

This shape keeps evaluations fast and predictable.

## Pre- and post-deployment, not post-deployment

Agentic evaluations target **candidate changes**: a new prompt, a new model, a new tool. They run on a captured trace set, not on live traffic. (For live traffic, see [Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md).)

## Output artifacts

* **Verdict** — pass/fail and severity per criterion
* **Root-cause report** — which trace, which span, which decision broke
* **Regression report** — what newly fails compared to baseline

## Where to use it

* CI gates on agent code/prompt changes
* Release-readiness reviews
* Auditor-friendly evidence in regulated industries

## Where to next

* [Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)
* [Stratix Premium — Agent Evaluation](/7.-observe-see-whats-happening/agent-evaluation.md)
* [Use case: Agentic evaluation](/8.-evaluate-score-the-outputs/agentic-evaluation.md)
* [Overview: Agentic evaluations](/4.1-general-use-cases/agentic-evals-overview.md)