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The pointEvals define 'done' before code is written.

Demo agents need 3 things. Production agents need 8.

What gets you to a demo isn't what gets you to production.

DEMO STACKPRODUCTION STACK
DEMO
Workflows
Define what the agent should do.
DEMO
Models
Pick the LLM and agent framework.
DEMO
Orchestration
Wire agents together (LangGraph, MCP).
PROD
Ground Truth Data
What the agent reads from — curated, versioned.
PROD
Evals
Pre-prod gate: what 'done' means.
PROD
Observability
Prod gate: what 'good' means.
PROD
HITL Patterns
Where humans approve, monitor, oversee.
PROD
Agent Agency Progression
How autonomy grows safely over time.
Tier filter:

BBaseline — enough for a demo·PProduction — required to ship·RRegulated — audit-grade additions

Deep Dive — Eval-Driven Development + AI Observability · Twin Substrates That Close The Trust Loop

Eval-Driven Development & AI Observability — The Trust Loop

Evals gate what ships · AI observability gates what runs · together they close the trust loop for agentic AI.

THE CONTRACTEvals define what ‘done’ means before code · observability proves what ‘good’ means in production.
EVAL SUBSTRATE · PRE-PRODUCTION

Gates what ships

Golden Datasets

Versioned, stratified by risk, refreshed from prod traffic. Bootstrap: synthetic + SME-curated examples before launch; expand from production traffic after.

Eval Rubrics

Pass/fail thresholds tied to KPIs — faithfulness, helpfulness, safety, regulatory alignment.

Judge Models

Pinned versions, inter-rater agreement, bias audits across subgroups.

Red-Team Corpus

Prompt injection, exfiltration attempts, jailbreaks — replayed every release.

OBSERVABILITY SUBSTRATE · PRODUCTION

Gates what runs

Live LLM-as-Judge

Judges run continuously on prod traffic, not just at gates — flag faithfulness/safety drift.

Drift Detection

Model + behavior drift caught early; auto-alerts to SRE & Model Owner with rollback path.

Trace Replay

Every agent decision is reconstructable for incident review and audit evidence.

Cost & Latency

Per-agent-task economics, live telemetry, ROI risk-adjusted by tenant and use case.

AI SOLUTION DELIVERY LIFECYCLE

CI → CD → CM → CO — with Continuous Evaluation (CE) running across every phase

BUILD & VALIDATE
OPTIMIZE & FEED BACK
CI
Continuous IntegrationBuild, test & validate AI solutions
CD
Continuous DeploymentDeploy safely with gates & controls
CM
Continuous MonitoringAlways-on quality, safety & cost monitoring
CO
Continuous OptimizationActively improve from production signals
CE · CONTINUOUS EVALUATION— always on across the lifecycle
CI·Pre-prod evals
Golden datasetLLM-as-judgeAdversarial / red-teamBehavioral tests
CD·Gate evals
Acceptance benchmarksShadow vs. prod compareCanary eval scoresPromotion gate
CM·Production evals
Live LLM-as-judgeHallucination rateHuman review samplingUser feedback signal
CO·Regression & refresh
Regression suiteGolden dataset refreshRed-team replaysEval drift → retrain
Traditional Software:
CI/CD is the main event
Monitoring = "is it up?"
Deterministic outputs
GenAI/Agentic AI:
Non-deterministic outputs
Model & prompt drift
Quality needs continuous evals
Cost is variable
Business
Technology
Stakeholders
These stages loop daily — Design ↔ Evals ↔ Dev.Solution Definition is a living artifact, not a phase gate.

SOLUTION DEFINITION

Architecture

System DesignSolution architecture
B
Integration PointsAPI & data flows
B
Tool RegistryActions, APIs, permissions
B
ScalabilityPerformance considerations
P

Requirements

Functional RequirementsWhat system does
B
Non-Functional Req.Quality attributes
P
Acceptance CriteriaDefinition of done
B

Data & Knowledge

Knowledge BaseRAG index, corpus
B
Data PipelinesIngestion, ETL, refresh
B
Data ContractsSchema agreements
P
Data Quality & LineageProvenance & freshness
P
Experiment RegistryRun tracking & artifacts
P

AI Design

Prompt EngineeringPrompt design
B
RAG PatternsRetrieval augmented
B
Agentic PatternsAgent architectures
B
Agent Persona DesignAgent identities
B
ExplainabilityTransparency & reasoning
R

Human Centred Design

Customer ExperienceUser-centric design
B
Stakeholder AlignmentBusiness buy-in
B
User JourneyEnd-to-end flow mapping
B
AccessibilityInclusive AI UX
P
Change ManagementAdoption & org transition
B

Risk & Compliance

Risk AssessmentWhat could go wrong?
B
Threat ModelingSecurity vulnerabilities
P
Security Req.Security needs
P
Compliance Req.Regulations
R
PII & PrivacyData minimization, redaction
P
Copyright & IPContent rights & attribution
P
Bias & FairnessEquitable outcomes
R
User Consent & Opt-outTransparency & controls
R
Iterative & Interconnected
Lead with Evals

Evals & Guardrails

Golden DatasetDefine ground truth first
P
Evaluation FrameworkWhat to measure & score
B
Guardrails DesignSafety boundaries & policies
P
Acceptance BenchmarksQuality gates for deployment
P

Dev & Validation

Testing SuiteUnit, integration, E2E
B
Regression SuitePrevent quality backslide
P
Pre-Prod EvaluationsRun defined evals
P
LLM as JudgeAI-powered evaluation
B
Agent Behavioral TestTest against boundaries
B
Adversarial TestingRed-team guardrails
R

Solution Readiness

Model CardsCapabilities & limits
P
System CardsSystem context & scope
P
RunbooksOps playbooks & on-call
P
ADRsArchitecture decisions
B
Go/No-Go ReviewGovernance sign-off
P
User TrainingAdoption support
B
External CollabPartner testing
B

Deployment & Rollout

Model & Prompt RegistryVersioning, promotion & rollback
P
AI Gateway ConfigSecurity & controls setup
P
Secrets & CredentialsKeys, tokens, rotation
P
Model ServingModel routing & caching
P
Agent RuntimeOrchestration & tool exec
P
HITL Approval GatesHuman review for high-risk
P
Environment PromotionStaging → production
P
Progressive DeliveryFlags, canary, shadow, A/B
P
Autonomy RolloutExpand agent decision scope
P
Multi-tenancy & IsolationPer-customer data & quota
P
Cost GuardrailsBudgets, quotas, rate limits
P
LaunchGo live

Continuous Monitoring

Agent Traces & AuditDecisions, tool-calls, reasoning
R
Error & Failure TrackingIncidents & exceptions
P
Incident Response & RollbackPlaybooks, kill switch, revert
P
Hallucination DetectionOutput quality failures
P
Prompt Injection DetectAdversarial input defense
P
Abuse DetectionMisuse & policy violations
P
Uptime & AvailabilityIs it running?
P
Performance & LatencyResponse tracking
P
Cost & UsagePer-agent cost tracking
P
Infra & Data DriftPipelines & distributions
P
User FeedbackRatings, thumbs, escalations
P
SLA AdherenceTargets met & breach alerts
P

Continuous Optimization

Prompt OptimizationData-driven refinement
P
Model OptimizationFine-tuning & routing
P
Agent Workflow TuningBehavior refinement
P
Retraining & RefreshModel retrain + RAG rebuild
P
Model DeprecationSunset & migrate old models
P
Cost OptimizationSpend vs quality
P
A/B & ExperimentationControlled AI tests
P
Feedback Loop → CIProduction → integration
P
AI
Solutions
CO → CI FEEDBACK LOOP — Optimization insights drive the next integration cycle

Who does what — stage accountability

RoleSolution DefinitionEvals & GuardrailsDev & ValidationSolution ReadinessDeployment & RolloutMonitoringOptimization
Product OwnerAAIA
Domain SMERR
AI EngineerRAARRRR
Platform TeamCAA
Risk & ComplianceCCCI
Legend:
RResponsible — Does the work
AAccountable — Owns the outcome
CConsulted — Provides input
IInformed — Kept in the loop
Domain SMEs own golden-label quality in Evals · Risk & Compliance consult on Deployment is mandatory for the regulated tier
Human-Centered
Agent-First
Risk-Aware
Continuous Lifecycle
Observable
Always Optimizing
AI Solution Delivery - AI Transformation Framework

© 2026 Ramesh Kaluri. All Rights Reserved.