Solution · AI Evaluation
Agent Evaluation, Replay & Backtesting: Certify Every Agent Version Against Your Real Historical Cases Before Production
Golden tickets, state-diff scoring, adversarial variants, and a release gate — so 'the demo worked' is never how you find out an agent broke.
What this solution does
An agent certification harness pulls historical tickets, tasks, approvals, and interaction traces; redacts them; converts them into replayable scenarios; generates adversarial variants; defines expected state changes, expected non-actions, and maximum allowed disclosure; then replays every candidate agent version before release. Scoring covers correctness, containment, escalation quality, latency, and privacy — with a failure taxonomy that becomes your improvement backlog.
The buyer problem
- ›'We aren't able to rigorously generate and test all production scenarios before launching' is the honest CIO answer
- ›Vendor benchmarks don't reflect your tickets, tools, or approval logic
- ›Every model or prompt change is a leap of faith
- ›L0 → L1 escalation quality is not measured, so nothing improves
- ›No failure taxonomy: 'it went wrong' but nobody knows why
Good-fit signals
- Support, service-desk, sales-ops, ITSM, or analytical agents already in pilot
- 180+ days of historical tickets / tasks / approvals available
- Willingness to define expected state changes and expected non-actions
- A release cadence you want to make safer, not slower
Pilot scope
In scope
- +Golden scenario set (100–500 cases) curated + redacted from your history
- +State-diff-based evaluation (correctness, containment, escalation, latency)
- +Adversarial + edge-case variant generator
- +Privacy / injection sub-suite
- +Release gate wired into your CI/CD
- +Failure-taxonomy dashboard + drift monitor
Out of pilot scope
- −Building the agent under evaluation (that's a separate engagement)
- −Full LLM foundation-model benchmarking
- −Replacement for user acceptance testing
Timeline: 8–12 weeks · Investment: Fixed-fee lab build; monthly harness retainer optional
Systems we typically integrate
- ServiceNow / Zendesk / Jira / Linear (case sources)
- Salesforce / HubSpot (CRM context)
- GitHub Actions / GitLab CI / Jenkins (release gate)
- Langfuse / LangSmith / Arize (traces + evals)
- Snowflake / BigQuery / Postgres (historical data)
- Slack (release gate notifications + overrides)
Where humans stay in the loop
- Every release gated by a named owner who reviews the eval report
- New scenario families require reviewer approval before entering golden set
- Override of a failing gate is time-boxed + logged with reason
- Quarterly re-baselining reviewed by product + risk owner
KPI scorecard
| Metric | Typical baseline | Pilot target |
|---|---|---|
| Pre-release regression pass rate | Not measured | >95% before promotion |
| Post-release incident delta | Baseline | 50–80% reduction |
| Escalation precision (L0 → L1) | Not measured | Baseline set + trending up |
| Privacy-leak test pass rate | Untested | 100% before release |
Risks and the controls we put in place
Overfitting to past logs
Adversarial variant generator + monthly out-of-distribution scenario injection.
Rare incidents missed
Failure-taxonomy dashboard surfaces long-tail categories; incident tickets auto-seed the golden set.
Weak business acceptance criteria
Success is defined per workflow with the process owner, not by generic accuracy.
Eval harness becomes a bottleneck
Runs on every PR in CI; typical wall-clock target under 20 minutes for the standard suite.
How we help
Real cases, not synthetic
We source golden scenarios from your actual tickets — the ones your agent will actually face.
State-diff, not just text match
We score what changed in the system of record, not just whether the reply 'sounds right'.
Release gate you can operate
The harness runs in your CI, owned by your team, with a report a non-engineer can read.
Drift is a first-class metric
Weekly drift dashboards show when reality diverged from the golden set — before customers notice.
Frequently asked questions
How big should the golden set be?+
100–200 scenarios is enough to catch most regressions in a bounded workflow. High-risk or high-volume workflows scale to 500+.
Can we backtest a vendor agent we didn't build?+
Yes, provided the vendor exposes an API or replay hook. If not, the harness surfaces that gap as a procurement finding.
What if our historical data is messy?+
Curation + redaction is part of the engagement. Messy data is normal; the harness accommodates it.
How does this differ from LLM benchmarks like MMLU?+
Foundation-model benchmarks measure the model. This harness measures your agent against your workflows.
Does it work for regulated workflows?+
That's the highest-value use. Privacy sub-suites and audit-trail generation are designed for regulated release gates.
Services that ship this solution
The capabilities we combine to put this pilot into production.
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AI Pilot Implementation
Our AI pilot implementation is a fixed-scope engagement delivering one production-grade AI agent in 30–60 days. Every pilot ships instrumented with evaluation harnesses, production monitoring, human-in-the-loop checkpoints, and the governance guardrails enterprise organizations require — no subscription fees, no retainer fog, just a working prototype integrated with your data.
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RAG & Enterprise Knowledge Systems
Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from your enterprise data to answer queries with up-to-date, accurate information. At Implement Agentic in Irvine, we build RAG systems and enterprise knowledge platforms that deliver ~91% recall@10 with hybrid retrieval — far above naive vector search.
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Insights for buyers evaluating this solution
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Task completion, factual accuracy, policy compliance, escalation precision, latency, cost, override rate, downstream business KPI. The panel beats any single accuracy score.
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Working output is the easy half. Eval suite, kill-switch, on-call, observability, drift monitoring, versioning, and change management — the operational layer that gets pilots actually shipped.
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