Score-only pipelines
A failed grade without an approved replacement does not change behavior.
LLM evaluation
LLM evaluation services grounded in production traces.
Define the rubric, evaluate responses and trajectories, capture the approved alternative, and compare a changed agent against a production-grounded baseline.
Evaluation that stops at diagnosis is incomplete.
Dashboards and frameworks flag regressions. They rarely give the agent a better example to follow next time.
Prompt sprawl
Packing every exception into the system prompt eventually weakens what already works.
Offline benches only
Lab sets miss production tools, policies, and messy user language.
No correction memory
Reviewer fixes stay in tickets instead of becoming eval-ready experiences.
Evaluation plus an experience layer
We connect LLM evaluation to retrieval-augmented human feedback: measure, correct, store outside weights, retrieve, re-measure.
Production-grounded eval
Reconstruct traces and define success from your workflow—not a generic leaderboard.
Human-approved replacements
Capture the trajectory or response that should replace the failure.
Before/after evidence
Compare task success, edits, escalations, latency, and cost on one focused flow.
What an evaluation service should cover
An evaluation engagement must answer more than whether a model passed. It should establish what counts as success, how regressions are caught, and which evidence changes a release decision.
Evaluation design
Define task-level success criteria, representative scenarios, and the mix of automated graders and human review needed for the workflow.
Regression workflow
Version scenarios, prompt/tool changes, and expected outputs so a release can be compared to a stable baseline.
Production feedback loop
Bring real failures back into the set, then decide whether a correction belongs in retrieval, a prompt, a tool policy, or a future training run.
Build the approved examples used in evaluation with golden datasets for AI agentsApply the same workflow to call outcomes through voice AI testing services
LLM evaluation service menu
An evaluation engagement should decide what changes, not only assign a score.
Service menu
Rubric design, production-trace scoring, human review, approved corrections, and a before/after comparison on one workflow.
Automated vs human review
Use automated graders for volume; use humans for policy, tool judgment, and ambiguous trajectories.
Supported inputs
OpenTelemetry exports, conversation logs, ticket threads, and tool-call traces with version metadata.
Sample report outline
Failure clusters, rubric scores, corrected examples, baseline comparison, and a rollout recommendation.
LLM vs agent evaluation
LLM evaluation can focus on responses; agent evaluation must also score tools, state, recovery, and escalation.
Buyer deliverables
Versioned rubric, scored sample, approved replacements, and a clear release decision for the scoped workflow.
Reusable resource
Download the AI agent evaluation rubric
A CSV scoring template for task outcome, tools, state, recovery, escalation, safety, cost, and latency.
Illustrative template—not client data. Adapt it to your policies, privacy controls, and approval process.
Who this is for
- Teams shipping AI agents who need reliable, repeatable evaluation
- Support, voice, and ops workflows where mistakes are expensive
- Leaders comparing fine-tuning vs retrieval of approved examples
- Anyone tired of eval tools that stop at a red score
What the sprint includes
A — Audit
Failure and opportunity analysis with a rubric tied to your definition of success.
B — Build
Corrected experience dataset + retrieval prototype for one production flow.
C — Prove
Blinded quality review and a clear rollout recommendation.
Proof over promises
We do not promise lift before examining the workflow. The first engagement exists to produce measured evidence.
Limitations
- A high score on an offline bench does not prove production readiness.
- Score-only reports without approved replacements rarely change agent behavior.
- Human review introduces latency and disagreement that must be governed.
- Before/after comparisons are only interpretable when the workflow and baseline stay fixed.
Methodology by LTTAI LTD
This page describes less than three’s production-trace review method: reconstruct failures, capture human-approved corrections, store them outside model weights, and measure whether scoped retrieval improves a defined workflow.
Primary references used for terminology and risk framing:
Common questions
What does llm evaluation include?
We connect LLM evaluation to retrieval-augmented human feedback: measure, correct, store outside weights, retrieve, re-measure.
Do you fine-tune our model?
No. Approved corrections stay outside model weights and can be retrieved at runtime. Fine-tuning can still complement the system for stable, common behavior.
What do we need to start?
A sample of production traces or conversation exports for one workflow, plus a working definition of success for that workflow.
How is success measured?
We compare a fixed baseline against a changed agent on the same workflow using task success, edits, escalations, latency, and cost where available.
Related
Book an LLM evaluation that starts from your traces.
Bring a sample of production failures. We will map patterns and propose one Agent Improvement Sprint.