Prove the system didn't get worse this release.
AI systems behave probabilistically. Every prompt change, model update, or data shift can degrade reliability. Before shipping, you need evidence: a versioned eval suite, a calibrated grader, and a regression gate that blocks changes when quality drops.
The Reliability Problem
AI systems are probabilistic. They behave differently across runs, against new data, and after changes.
A prompt change that improves one use case can silently fail on another. A new model version that scores higher on benchmarks may degrade real-world behavior. Retrieval index updates can introduce hallucinations. Agent policy tweaks can break tool calling.
Without structured evaluation, teams ship unreliable changes. Without regression testing, failures go undetected. Without a quality gate, problems make it to production.
Crescent builds evaluation-first workflows: a versioned dataset, a grader layer, repeat-run measurement, and a release gate that catches regressions before they ship.
Why AI Reliability Is Different
Traditional software testing assumes deterministic behavior. AI doesn't.
Probabilistic behavior
The same input may produce different outputs across runs. Reliability measures consistency and failure rates, not pass/fail.
Model changes
Upgrading a model or switching providers changes behavior. You need benchmarks to know if it improved or degraded.
Prompt changes
Small prompt edits can shift behavior dramatically. A new instruction can break edge cases or cause refusals.
Agent behavior
Agents compose models, tools, and policies. A change in any layer ripples through the system. You need regression testing at the whole-system level.
Data changes
New production data, retrieval index updates, or tool schema changes alter behavior. Reliability means measuring impact before deploying.
Reliability Failure Modes
What actually breaks in production, and why evaluation catches it.
Hallucination
Model generates false information. Eval detects with grounded-answer checks and retrieval metrics.
Tool misuse
Agent calls tools incorrectly or with wrong arguments. Eval measures tool-call F1 and argument accuracy.
Refusal failure
Model refuses valid requests or accepts invalid ones. Eval enforces refusal boundaries.
Consistency drift
Same question produces different answers across runs. Eval repeats requests and measures variance.
Escalation failure
System should escalate to a human but doesn't. Eval tests escalation logic and thresholds.
Retrieval quality drop
Index update or query change reduces relevant documents. Eval measures context precision and recall.
Reliability Architecture
Six layers that make evaluation production-grade.
Evaluation dataset
Versioned collection of test cases covering normal use, edge cases, and failure modes. 50-100 normal cases, 10-20 stress cases per flow.
Regression testing
Automated suite that runs before every change — prompt, model, retrieval, tool, agent policy. Blocks deployments when scores drop below threshold.
Testing framework
DeepEval, Ragas, or promptfoo — structured evaluation with metrics (task success, hallucination rate, tool accuracy, refusal correctness, consistency).
Production monitoring
Continuous measurement of eval metrics against live traces. Detects degradation post-deployment.
Failure detection
Automated flagging of regressions. LLM-as-judge for open-ended quality, deterministic checks for deterministic outputs.
Recovery & rollback
Automated or manual rollback when eval scores drop. Incident postmortem tied to the eval suite for learning.
What Crescent Engineers
We design and operate the complete evaluation infrastructure.
Use-case contracts
We define what "good enough" means for each workflow: expected outputs, forbidden behaviors, pass thresholds, severity levels, and remediation paths.
Eval datasets
We build versioned test sets from your production traces, public benchmarks, and edge cases. We calibrate graders against your domain and business requirements.
Regression gates
We build CI/CD pipelines that run evaluations on every change. If scores drop, the change is blocked until fixed or approved as an exception.
Monitoring dashboards
We instrument production traces so eval metrics flow back automatically. Degradation is caught immediately, not weeks later.
Incident triage
We tie production failures back to the eval suite. If an incident happens, we know whether the eval was too loose, or whether a new pattern wasn't covered.
Continuous calibration
As your system evolves, we update graders and thresholds. Eval suites drift — we maintain them.
Reliability Workstreams
How the work breaks down across your org.
Product
Define use-case contracts. What counts as success for each workflow? What outputs are forbidden? What does acceptable cost and latency look like?
Data
Build and maintain the eval dataset. Start with production traces. Expand with public benchmarks and manually curated edge cases.
ML / AI
Build graders. Implement scoring rubrics. Calibrate LLM-as-judge against human labels. Define thresholds for each workflow.
Infrastructure
Wire eval framework into CI/CD. Build dashboards. Set up alerts. Integrate with your deployment pipeline so regressions are caught before release.
Operations
Monitor production traces. Run recurring eval measurements. Triage failures. Update thresholds as behavior changes.
Quality Gates
When evaluation runs and what it decides.
On prompt change
Run eval suite. If task success stays ≥ threshold, approve. If drop detected, flag for review or block.
On model upgrade
Benchmark new model against current. Compare across all workflows. If any metric degrades, hold upgrade.
On retrieval index update
Measure context precision and recall. Repeat queries. If retrieval quality drops, rollback index or re-calibrate.
On tool or agent change
Run whole-system eval. Test tool calls, arguments, escalations. If end-to-end success drops, reject change.
Continuous production monitoring
Every X hours, run evals on the last N production traces. Alert if live metrics diverge from baseline.
Production Monitoring
After deployment: continuous measurement.
Eval suites run before release. After release, you instrument traces so metrics flow back automatically.
Baseline metrics
Task success, hallucination rate, tool accuracy, refusal correctness, consistency, cost per task.
Drift detection
Alert if metrics diverge from baseline. Investigate immediately — could be data shift or a silent bug.
User-reported issues
When support or product reports a problem, run eval on that case. Was it already detectable? Why did monitoring miss it?
Postmortem & learning
Every incident teaches you where eval coverage is weak. Update test sets and thresholds to catch the next one earlier.
Failure & Recovery
What happens when an eval catches a regression.
Pre-release (CI/CD gate)
Eval fails → change is blocked. Author must fix or request exception. Exception requires documented risk and approval.
Post-release (monitoring)
Production metrics drop → alert fires. Team reviews traces, root-causes the change, and either reverts or patches.
Automated vs. manual rollback
For critical workflows, we configure automated rollback when eval scores drop below a hard threshold. For others, the alert requires human review.
Root cause & prevention
After recovery, we triage: was the eval incomplete? Was a threshold too loose? Did the eval miss a use case?
Deliverables
What you own after the engagement.
Use-case contract
Documented definition of success for each workflow
Eval dataset
Versioned test set (normal cases, edge cases, stress cases)
Grader implementation
Scoring logic and LLM-as-judge calibration
Regression suite
CI/CD-integrated test harness ready to run on every change
Eval framework choice
DeepEval, Ragas, or promptfoo — configured for your stack
Quality gates
Release policies: when eval runs, what blocks, what approves
Monitoring dashboards
Production eval metrics flowing from live traces
Playbook & runbook
How to triage eval failures, update datasets, handle incidents
Training & handoff
Your team owns, updates, and maintains the suite going forward
Reliability Metrics
What we measure depends on your workflow. These are common.
Task success
Percentage of requests that produce acceptable output
Hallucination rate
Percentage of outputs containing false information
Tool accuracy
Percentage of tool calls with correct function and arguments
Refusal correctness
Percentage of refusals that were appropriate
Consistency
Variance in outputs across multiple runs of the same input
Retrieval quality
Context precision (% relevant docs) and recall (% of relevant docs returned)
Escalation correctness
Percentage of escalations that were necessary and sufficient
Cost per passing task
Inference cost normalized by task success
Who Needs It
Five profiles that depend on reliability engineering.
Case Studies & Evidence
The proof lives in the deliverables you produce.
We don't have generic client case studies for production AI reliability work yet. Instead, the evidence is the artifacts we build during delivery:
Evaluation Report
Baseline eval results on your initial dataset. Exact metrics, thresholds, failure modes, and opportunities.
Regression Suite
The actual test harness running against your workflows. Auditable, repeatable, CI/CD-integrated.
Monitoring Dashboard
Live production metrics flowing from real traces. Proof that eval catches real degradation.
Incident Postmortems
When production issues happen, we tie them to eval coverage gaps. Learning shows in test-set updates.
These become your competitive advantage: you can ship faster, more confidently, knowing regressions are caught before they reach users.
Related Services
Reliability often pairs with these.
FAQ
Common questions about reliability engineering.
Build the eval suite that catches failures before they ship.
Tell us your workflows, data constraints, and risk tolerance. We'll design an evaluation-first system tailored to your needs — then train your team to own it.
NDA available on request · Scoped engagements · No surprise fees