Score-only pipelines
Failed grades without approved alternatives.
Automated eval vs human review
Automated eval vs human review: scores without approved replacements do not change behavior.
Automated graders scale volume. Human review captures the approved correction your agent should follow next time. Production agent improvement needs both—connected.
Automated eval alone stops at diagnosis.
A red score tells you something failed. It rarely stores the trajectory or response that should replace the failure in production.
Grader blind spots
Policy, tool judgment, and escalation paths need humans.
Synthetic mismatch
Lab scenarios miss production language and tool state.
No correction memory
Review findings stay in dashboards—not in the agent.
Connect eval to an experience layer
Automated graders filter volume; human reviewers approve replacements; approved records feed retrieval and re-measurement.
Graders at scale
Run rubrics on high-volume traces and regressions.
Human approval gates
Experts sign off on corrections, scope, and retirement.
Closed loop
Bring new production failures back into eval and retrieval sets.
Split the work deliberately
Use machines for throughput. Use humans for judgment, policy, and approval.
Automate
Regression detection, rubric scoring, duplicate failure clustering.
Human review
Ambiguous trajectories, compliance paths, tool escalation decisions.
Store outcomes
Only approved corrections enter the experience library.
Reusable resource
Get the AI agent evaluation rubric
CSV 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 should read this
- Teams with eval dashboards but flat production quality
- Regulated workflows requiring human approval gates
- ML leads building regression gates before release
Eval plus approved corrections
A — Audit
Rubric design tied to production failure clusters.
B — Build
Grader workflow + human approval + retrieval prototype.
C — Proof
Baseline vs corrected comparison on one workflow.
Eval that changes the next run
We measure whether approved corrections retrieved at runtime improve the same workflow the rubric scores.
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 automated eval vs human review include?
Automated graders filter volume; human reviewers approve replacements; approved records feed retrieval and re-measurement.
Do you fine-tune our model?
Not first. Approved corrections for exceptions stay outside weights and can be retrieved at runtime. We train a speaker adapter when a frozen 2x2 shows the prompt cannot install identity.
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 eval review grounded in your traces.
Send sample failures. We will map automated vs human review split for one workflow.