Training latency
By the time a fine-tune ships, the failure cluster and policies may have already changed.
Improve agents without fine-tuning
Improve AI agents without fine-tuning—using approved production corrections.
When the same failures keep returning, you do not need another training run first. Capture human-approved replacements from traces, retrieve them for similar requests, and prove lift on one scoped workflow.
Fine-tuning is the wrong first move for repeating exceptions.
Training embeds behavior slowly and opaquely. Repeated production exceptions need a faster, inspectable correction loop.
Opaque corrections
Individual approved answers disappear inside weights and cannot be audited or retired cleanly.
Prompt packing
Stuffing every exception into the system prompt weakens what already works.
Score-only eval
A red grade without an approved replacement does not change the next similar run.
A retrieval-first improvement loop
We reconstruct failures, capture approved corrections outside model weights, retrieve scoped examples at runtime, and re-measure before wider rollout.
Trace-grounded corrections
Start from real failures with tools, state, and outcomes—not synthetic guesses.
Experience library
Keep rewrites, critiques, scope, version, and approval status inspectable and removable.
Measured before/after
Compare task success, edits, escalations, latency, and cost on one workflow.
When to skip fine-tuning first
Fine-tuning still helps for stable, common behavior. For expensive repeating exceptions, a governed experience library is usually the faster path to proof.
Exception-heavy workflows
Support, voice, claims, and compliance paths where humans already rewrite agent output.
Need for auditability
Regulated or high-risk flows that must show which example influenced a decision.
Release pressure
Teams that need measured lift this sprint—not after the next training cycle.
Store corrections as golden datasets for AI agentsBuild the library from production traces
No-fine-tune improvement package
Prove whether approved retrieval beats waiting on a training cycle.
Decision memo
Which failure clusters belong in retrieval now vs a later fine-tune.
Experience library slice
Approved corrections with scope, version, critique, and retirement rules.
Retrieval prototype
Scoped few-shot injection for one workflow with filters and thresholds.
Before/after pack
Task success, edits, escalations, latency, and cost on a fixed baseline.
Rollout controls
Approval gates, negative retrieval tests, and expand/stop criteria.
Reusable resource
Start from an illustrative golden-dataset record
Inspect fields for source trace, failure, approved correction, critique, scope, version, and approval status.
Illustrative template—not client data. Adapt it to your policies, privacy controls, and approval process.
Who this is for
- Teams blocked on fine-tune cycles while the same failures keep shipping
- Operators who already edit agent output and need those edits to compound
- Leaders who want measured lift before committing to training infrastructure
- Compliance-sensitive workflows that need removable, scoped corrections
Agent Improvement Sprint without a training run
A — Audit
Cluster repeating failures and decide which belong in retrieval vs a later train.
B — Dataset
Human-approved experience library + retrieval prototype for one workflow.
C — Proof
Baseline vs corrected comparison and a clear rollout recommendation.
Prove lift before you train
We do not promise improvement before examining the workflow. The first engagement exists to produce evidence—and to decide whether fine-tuning is still needed later.
Limitations
- Stable, high-volume behaviors may still need fine-tuning later.
- Retrieval without scope filters can transfer the wrong exception.
- Stale approved examples can hurt performance if not retired.
- Measured lift on one workflow does not guarantee every adjacent intent.
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 improve agents without fine-tuning include?
We reconstruct failures, capture approved corrections outside model weights, retrieve scoped examples at runtime, and re-measure before wider rollout.
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 a no-fine-tune improvement review.
Send a sample of production traces. We will map which failures can improve through approved retrieval first.