Artificial Intelligence
July 17, 2026
AI Agent Evaluation
Know Your AI Agent Works Before Your Users Find Out It Doesn’t
Codiste builds the evaluation layer for AI agents. We catch failures before deploy and monitor quality in production, so you can ship changes with confidence, not hope.
Reliability Decides Which Agents Survive
The gap between a demo that impresses and an agent that survives production is measurement. The industry numbers make the case better than we can.
40%+
of agentic AI projects will be canceled by 2027, according to Gartner.
~95%
of enterprise GenAI pilots show no measurable business impact, per MIT.
60%
success across ten steps is all a 95%-per-step agent achieves. Small errors compound fast.
Most Agents Don’t Fail Loudly. They Fail Quietly.
They drift, misroute, and make things up in ways a dashboard hides until a customer or an auditor finds it first. Standard tests miss exactly the failures that break agents.
95% → 60%
A step that is 95% reliable, run ten times in sequence, lands near 60% end to end. Per-step correctness has to be measured, not assumed.
- Right answer, wrong path
- The output looks correct while the reasoning underneath is broken. The agent reached a valid result by an invalid route, so the next input breaks it.
- Silent regressions
- One change improves a flow and quietly breaks another. Without a gate on every deploy, nobody notices until the affected users do.
- Fabrication
- The agent reports that an action succeeded when the underlying tool call actually failed. A "booking confirmed" that never reached the booking system.
- Ungrounded answers
- Instead of saying it does not know, the agent invents facts. Confident, fluent, and wrong is harder to catch than an obvious error.
- Fake metrics
- An uncalibrated LLM judge hands you confident noise. You end up optimising against a score that does not track real quality.
One System That Guards Deploys and Watches Production
Two connected tracks, not two disconnected tools. The offline gate stops bad changes reaching production. The online track watches what real traffic does once they land.
Track A
Offline: the gate before deploy
Runs on every change, against your real agent, before anything reaches a user.
- 01
Golden datasets
Versioned test suites drive your real agent, with tool edges mocked so runs stay fast and deterministic.
- 02
The CI gate
Any change that drops quality below threshold is blocked before merge. Most teams see value here within 2 to 4 weeks.
- 03
Regression caught
The break is found against your real agent rather than a stub, so it never reaches a production user.
Track B
Online: the watch after deploy
Runs continuously on live traffic, because real users find what test suites do not.
- 01
Traffic sampling
Once a change ships, we sample live traffic to watch what real users actually trigger.
- 02
Tiered scoring
Scoring runs in tiers so coverage stays high while evaluation spend stays predictable.
- 03
Failure becomes a test
Every production failure is written back into the golden datasets on the offline track, so the same problem cannot come back.
The online track writes every production failure back into the golden datasets that gate the next deploy. That single edge is what turns two tools into one system: the gate gets stricter every time production teaches it something new.
Score Failures Where They Happen, So You Can Fix Them
An agent can fail at the tool call, at the sequence of steps, or at the final answer. Each needs a different check, so we score all three.
Level 1
Component
Did it pick the right tool and call it correctly?
Routing, tool choice, argument correctness, and API contract compliance.
Level 2
Trajectory
Did it take a sensible path to get there?
The right tool sequence, clean error recovery, and correct handoffs between steps.
Level 3
Outcome
Did the user actually get what they needed?
Correctness, completeness, hallucination, tone, and brand voice in the final response.
Score only the outcome and you cannot tell a lucky answer from a correct process. Score only the components and you miss whether any of it added up. All three levels together are what makes a score worth acting on.
A Judge You Have Not Calibrated Is Just Confident Noise
This is the moat.
Most teams score their agents with an LLM judge and never check whether that judge agrees with a human. So the dashboard turns green while quality drifts, and the number everyone is optimising against measures nothing.
We treat every judge as something that itself has to be tested. We build a human-rated calibration set from your own traffic, measure how closely the judge agrees with it, and tune the rubric until agreement clears the threshold. Deterministic checks handle everything that can be verified outright, so the judge is only asked to rule on what genuinely needs judgement.
80%+
human agreement required before a judge is trusted to gate anything.
75%+
of checks run deterministically, where a pass or fail is a fact rather than an opinion.
We Run This System on Our Own AI Voice Agent
Dialora is Codiste’s own AI voice agent platform, handling live phone calls for telecom, hiring, and business support.
Exactly the kind of high-stakes, multi-step agent that fails quietly, so it is where we pressure-tested this evaluation system before offering it to anyone else.
Golden datasets for real call flows
Bookings, qualification, escalations, and the messy edge cases that only show up on live phone calls.
Deterministic checks on every tool call
A "booking confirmed" only counts when the booking actually went through. The agent cannot claim credit for a failed call.
Calibrated judges on sampled live calls
Tone and groundedness scored by judges tuned against human ratings before we trusted a single number they produced.
Value Early, Risk Low
Take it as a one-time build or an ongoing managed service. Either way, you own the datasets, the harness, and the judges.
- 1 to 2 weeks
Assessment and metric design
We map your agents, rank them by production risk, and define what "correct" means as measurable metrics.
- 2 to 4 weeks
Offline CI gate
Golden datasets and a runnable harness for your highest-risk agents, wired into CI so regressions never reach production.
- 2 to 3 weeks
Online monitoring
Live traffic sampling, tiered scoring to control cost, dashboards, and regression alerts on the channels you already watch.
- 2 to 3 weeks
Judge calibration and loop
We measure each judge against human ratings and tune until it clears 80%+ agreement, then keep the loop running.
Timelines flex with the number of agents, how complex their flows are, and how quickly we can get access to your traffic. The phases above assume one high-risk agent to start.
Assets You Own, and a Team That Can Run Them
Everything we build is handed over, documented, and runnable by your engineers without us.
- Evaluation framework
- Failure taxonomy, metrics, thresholds, and a rollout plan matched to how your agents actually fail.
- Golden datasets
- Versioned test suites per agent, covering happy paths and the messy edge cases. Yours to keep.
- Runnable eval harness
- Drives your real agent rather than a stub, plus the CI gate that blocks quality regressions on every change.
- Online monitoring
- Sampling, tiered scoring, dashboards, and regression alerts on live production traffic.
- Calibrated judges
- Every judge shipped with a measured human-agreement score, plus the calibration set behind it, so you can re-run the check yourself.
- Handover and enablement
- Documentation and training so your engineers can run, extend, and own the whole system without us.
All of it is yours. The datasets, the judges, the harness, and the calibration sets stay in your environment and keep working whether or not we stay involved.
Ready to Make Your Agents Measurable?
Tell us which agent causes the most production issues today. In your assessment, we'll map your highest-risk flows, design your evaluation framework, and scope a 2 to 4 week path to your first CI gate, so you ship confidently.
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Get the Clarity You Deserve
Measuring whether an agent behaves correctly, not just whether the final answer looks right. We check whether it took the right path, used the right tools, and stayed grounded, reliably, across many cases.
Normal software is deterministic. Agents are not, and their errors compound: 95% reliability per step becomes roughly 60% over ten steps. We score behaviour at multiple levels to catch what standard tests miss.
Those are observability tools. They surface issues but do not prevent them or calibrate your judges. Codiste adds the evaluation layer on top: deterministic checks plus calibrated LLM judges, so you catch regressions before deploy and can actually trust your metrics.
We mock sensitive tools in the offline harness and run online sampling with data masking. You own all datasets and judges, and nothing leaves your environment without your approval.
Two models: a one-time build with a fixed scope and handover, or an ongoing managed service on a monthly retainer. We price the build from your assessment scope, and additional agents or online monitoring scale from there. You get an exact quote after the free assessment.
An assessment takes 1 to 2 weeks, and a working CI gate on your highest-risk agent usually lands in 2 to 4 weeks.
Tell Us Which Agent Causes the Most Issues
Every great partnership begins with a conversation. Tell us which agent or flow causes the most production issues today, and we’ll scope it with you.