No scope without traces
We do not promise lift before reviewing your workflow sample.
Agent Improvement Sprint
Agent Improvement Sprint: audit, dataset, retrieval prototype, and measured proof.
A focused engagement to turn production failures into governed golden datasets and prove whether scoped retrieval improves one workflow—before you fine-tune or rewrite prompts again.
Why teams book a sprint instead of another tool trial.
Dashboards diagnose. Training embeds. Prompts sprawl. The sprint produces inspectable corrections and measured before/after evidence on your traces.
One workflow first
Depth on a high-cost path beats shallow coverage everywhere.
Sales-led scoping
Fees and timeline are agreed after trace review—not from public price lists.
Evidence over hype
Deliverables are artifacts you can inspect, not slide-deck outcomes.
What the sprint includes
Four phases over a typical 4–6 week engagement, depending on trace access and reviewer availability.
A — Audit
Failure clustering, rubric design, and annotation protocol tied to your success definition.
B — Dataset
Human-approved experience library with provenance, scope, version, and approval status.
C — Prototype
Retrieval integration for one workflow with filters, thresholds, and fallbacks.
Timeline and phases
Exact timing depends on trace quality, reviewer bandwidth, and integration complexity. This is the default shape.
Week 1–2: Audit
Trace sample intake, failure patterns, rubric, and sprint success criteria.
Week 2–4: Build
Reviewer loop, approved records, retrieval prototype on one workflow.
Week 4–6: Proof
Baseline vs corrected comparison, rollout recommendation, and coverage gaps.
Reusable resource
Get the trace readiness checklist
Confirm you have the export fields and reviewer access needed before a sprint kickoff.
Illustrative template—not client data. Adapt it to your policies, privacy controls, and approval process.
Who this is for
- Teams with production traces and repeating failure modes
- ML/AI leads who need measured lift before fine-tuning spend
- Support, voice, healthcare, or claims workflows with human review today
- Operators who need inspectable corrections—not opaque model changes
Who this is not for
- Teams without production traces or reviewer access
- Buyers seeking guaranteed outcomes before sharing data
- Greenfield demos with no live failure history
Deliverables you receive
Trace audit report
Failure clusters, rubric, and annotation protocol for one workflow.
Golden dataset v1
Approved records with provenance and governance fields.
Before/after proof
Comparison on task success, edits, escalations, latency, and cost where available.
What we need from you
A minimized production trace sample, a working definition of success, and access to domain reviewers—or agreement to use ours under your rubric.
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 agent improvement sprint include?
Four phases over a typical 4–6 week engagement, depending on trace access and reviewer availability.
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 a trace review to scope your sprint.
Send a sample of production traces. We will estimate experience coverage and propose one focused sprint.