How to Build Eval-Driven AI Observability for Agents | Scorable

Updated: 2026-05-10
By: Ari Heljakka

Short answer

Eval-driven AI observability is the practice of treating evaluations (evals) as first-class signals alongside latency and errors, so every change to a prompt, model, or tool is gated by measurable quality on a representative dataset. It works because non-deterministic systems break the classical test-debug loop, and the only way to make iteration tractable is to score outcomes continuously on real traces and golden examples.

Key facts

• Definition: A production observability methodology in which agentic traces are automatically scored by evaluators (LLM-as-judge, deterministic checks, or programmatic asserts), and those scores drive development, deployment, and rollback decisions.
• When to use: Production AI agents, RAG systems, classification or extraction pipelines, and any LLM feature where correctness matters more than uptime.
• Limitations: Adds infrastructure complexity, requires curated datasets to be useful, and is not a fit for free-form general assistants with no recurring patterns or for real-time code-execution paths where the evaluator latency exceeds the request budget.
• Example: A RAG agent in production emits traces; faithfulness, answer relevance, and tool-call quality scorers run on a sampled subset; regressions are clustered automatically and surfaced as a queue of failure groups instead of raw bad outputs.

Key takeaways

• AI agents demand a separate notion of correctness from operational uptime: "is it running?" and "is it right?" are independent questions.
• Eval-driven development is the agent equivalent of TDD: develop, evaluate, iterate, with eval scores acting as the gating signal.
• Three primitives are non-negotiable in production: agentic tracing, golden datasets, and experimentation at scale.
• Eval-driven observability is overkill for some teams and use cases; the limitations section below names them explicitly.

Definition

Eval-driven AI observability is the combination of two concepts:

1. Observability for AI agents, meaning end-to-end traces of model calls, tool calls, and intermediate reasoning, captured with enough fidelity that any production output can be reconstructed and audited.
2. Eval-driven development (EDD), a feedback-loop methodology in which evaluations are run on those traces, and on a curated golden dataset, and the resulting scores are treated as signals on par with errors and latency.

Operational performance answers "is the system up, fast, and error-free?" Functional performance answers "is the output correct, grounded, and useful?" Eval-driven observability is what makes the second question measurable.

When this matters

Eval-driven observability pays back the setup cost in production AI systems with recurring patterns of behavior:

• Customer-facing RAG assistants where faithfulness and answer relevance are core to the product.
• Tool-using agents where wrong tool selection or malformed arguments cause silent business-logic failures.
• Classification, extraction, and summarization pipelines whose outputs feed downstream automation.
• Compliance- or safety-sensitive surfaces where every output must be auditable.

It does not earn its keep in:

• Free-form general assistants where each conversation is unique and no recurring eval criteria apply (see Limitations).
• Real-time code evaluation paths where any judge call adds latency the request cannot absorb.
• Throwaway prototypes that will never see real users.

How it works

The loop has three repeating steps and three supporting primitives.

The Eval-Driven Development loop

1. Develop. Change a prompt, swap a model, add or remove a tool, or update retrieval logic.
2. Evaluate. Run the eval suite (LLM-as-judge scorers, deterministic checks, regression tests) on a representative dataset and on a sample of recent production traces.
3. Iterate. Read the eval scores like a test report. Ship the change if scores improve, roll back if they regress, investigate the trace clusters that moved.

Treat evaluators like code: version-controlled, logged, comparable across runs, wired into CI/CD so a prompt change cannot reach production if it tanks the gating metrics.

Primitive 1, agentic tracing

Captures every model input, model output, tool call, retry, and intermediate plan, stitched into a single trace per user request. Without this, root-cause analysis of a wrong answer reduces to guesswork. With it, the trace is the artifact you reach for the moment a score drops.

Primitive 2, golden datasets

A living set of input + expected-behavior pairs, curated from:

• Real production traces, especially failures already seen.
• Adversarial cases the team explicitly cares about.
• Edge cases reported by users that should never recur.

The dataset evolves as the product does. New failure modes flow back in; future changes must clear that bar before shipping.

Primitive 3, experimentation at scale

Every prompt tweak, model swap, or tool change is logged as an experiment with a hypothesis, a configuration, and a result. Comparisons across experiments become apples-to-apples, and the team accumulates a queryable record of which changes moved which metrics.

Automation that compounds the loop

Once the basics are in place, three automations pay back the investment:

• Prompt optimization, automated search across prompt variants, scored against the eval suite.
• Judge autotuning, judge models calibrated against human labels rather than hand-tuned by intuition.
• Production root-cause clustering, failures grouped into actionable themes instead of a flat list of bad traces.

Example

A representative end-to-end setup for a RAG agent in production:

1. Every request emits a trace: query, retrieved context, generated answer, tool calls, latency, model versions.
2. A sampler picks (say) 10% of traces. Three scorers run on each:
   • Faithfulness, does the answer invent facts not present in the retrieved context?
   • Answer relevance, does the answer address the asked question?
   • Tool call quality, was retrieval triggered correctly, with a sensible query?
3. Each scorer returns a structured

   {score, justification}

pair. Scores are stored alongside the trace. 4. A dashboard shows score distributions over time, with alerts when any metric drops more than N standard deviations from baseline. 5. A nightly job runs the same scorers across the golden dataset, gating tomorrow's deploy if regressions appear.

The same setup, in spirit, applies to extraction pipelines, classification agents, and tool-using agents; the scorers change, the loop does not.

Who this approach is not for

The eval-driven loop is broadly useful, but it is not universal. It is probably not the right approach for any of the following situations:

1. Data-science teams who want to DIY everything. If the preferred path is to author every scorer, dataset format, dashboard, and storage backend in-house and treat any platform as an obstacle, a packaged version of this loop slows the team down more than it helps.
2. Teams looking for real-time code evaluations inside the request path. Eval-driven observability is a sampled, asynchronous loop. If the requirement is to evaluate generated code synchronously inside a sub-second user request, evaluator latency will dominate and a different architecture is needed.
3. Builders of free-form general assistants with no repeating patterns. If every conversation is unique, there are no stable rubrics to score against, golden datasets do not converge, and the eval loop produces noise rather than signal.

If any of those describe you, the rest of this guide is still useful as a mental model; just don't expect any tooling purchase to fix it.

Limitations

Even when eval-driven observability is the right approach, it has well-known soft spots:

• Judges drift. Frontier models change, prompts change, and a once-calibrated judge can quietly slip. Calibration is a recurring task, not a one-off.
• Datasets rot. Golden examples reflect last quarter's product. Datasets need active curation, or they slowly stop measuring what you ship.
• Score is not truth. A high score can still hide systemic failure modes the eval suite was not designed to catch. Eval scores are a strong signal, not a verdict.
• Cost adds up. Sampling, judge calls, and storage compound. Without a sampling strategy, observability spend can rival inference spend.

Evidence and sources

Primary source

• Shri (Product Lead, Datadog), "Practical AI-Enabled Observability for Agents and LLMs," YouTube, 2026-04-07. Video: https://www.youtube.com/watch?v=Xe60gkyDtGw. Channel: https://www.youtube.com/channel/UCPO2QgTCReBAThZca6MB9jg.

FAQ

What is the difference between AI observability and traditional observability?
Traditional observability tracks operational signals such as uptime, latency, and errors. AI observability adds functional signals (correctness, faithfulness, tool-call quality) captured by evaluators that score the agent's outputs and intermediate decisions.

Do I need a golden dataset before I can start?
No. Start with ten realistic examples drawn from production traces and one end-to-end eval. The dataset grows as failures arrive; do not block the loop on a perfect dataset.

Can I run eval-driven observability without an LLM-as-judge?
Partially. Deterministic checks (regex, schema validation, exact-match against expected outputs) cover a portion of the surface. LLM judges become essential when you need to score open-ended properties like faithfulness or helpfulness.

How much production traffic should I sample for evals?
Start at 5–10%, biased toward suspected failure modes. Increase coverage on critical surfaces; decrease on stable, low-risk paths. Sampling strategy is a cost lever, not a fixed setting.

Is eval-driven observability the same as guardrails?
No. Guardrails block or transform outputs in real time inside the request path. Eval-driven observability is asynchronous: it scores what shipped, drives iteration, and informs which guardrails are needed in the first place.