Quality Engineering AI Training

Testing & Evaluation of GenAI and Agentic Systems

Systematic testing, evaluation, and quality engineering frameworks for validating GenAI and agentic AI systems at enterprise scale.
feature
  • Level : Intermediate to Advanced
  • Duration : 32 hours
  • Hands-on Ratio : 70%
  • Overview
  • Curriculum
  • Feature
  • Contact
  • FAQs

Building Strategic Influence in Matrix Organizations

Testing GenAI systems requires new approaches beyond traditional software validation. Testing & Evaluation of GenAI and Agentic Systems focuses on ensuring reliability, accuracy, and safety of AI-driven applications.

The course introduces evaluation frameworks for prompts, responses, agents, and workflows. Participants learn how to define quality metrics, test edge cases, and monitor performance over time.

By the end of the course, teams can establish repeatable testing practices that support confident deployment and continuous improvement of GenAI and agentic systems.

Recommended participant setup

icrosoft Foundry access, sample GenAI application or agent, evaluation datasets, repository for test automation, Log Analytics workspace 

AI-First Learning Approach

This course follows Cognixia’s AI-first learning model, combining concept briefings with intensive hands-on labs, system-level testing scenarios, and evaluation artifacts that mirror real enterprise GenAI deployments.

Business Outcomes

Organizations enrolling teams in this course can achieve

  • Higher Release Confidence: Structured evaluation gates reduce the risk of regressions, safety issues, and unreliable agent behavior
  • Enterprise-Grade Quality Engineering: Standardized datasets, evaluators, and reports support consistent testing across teams and products
  • Continuous Quality Visibility: Ongoing evaluation and monitoring detect drift and emerging failures early in production

Why You Shouldn’t Miss this course

By the end of this course, participants will be able to:
  • Define / Design evaluation strategies aligned to enterprise acceptance criteria for GenAI and agentic systems
  • Build golden, regression, and adversarial datasets for LLM applications and agents
  • Implement automated evaluators for correctness, groundedness, safety, tool usage, and workflow completion
  • Analyze / Diagnose failures using error taxonomies, root-cause analysis, and trace-linked evidence
  • Operationalize evaluation through CI/CD gating, experimentation, and continuous production monitoring

Recommended Experience

Participants should have basic Python skills, familiarity with QA and test design practices, and foundational understanding of GenAI concepts to effectively engage with evaluation engineering exercises.

Structured for Strategic Application

Bloom-aligned objectives
  • Understand: evaluation dimensions for GenAI systems
  • Analyze: failure modes across chat, RAG, and agents
  • Design: an evaluation blueprint tied to user acceptance criteria
Topics
  • Why LLM testing differs from deterministic software testing
  • Evaluation pyramid for GenAI:
    • unit checks (schemas, validators)
    • component evaluation (retrieval, prompt response quality)
    • end-to-end scenario evaluation (multi-turn, tool workflows)
  • Defining acceptance criteria:
    • correctness and usefulness
    • groundedness/citations
    • safety and compliance
    • performance and cost
Labs
  • Lab 1.1: Evaluation blueprint — Define evaluation dimensions, metrics, and minimum thresholds for a target use case.
  • Lab 1.2: Failure mode mapping — Create a failure taxonomy for the target app (RAG + tool agent).
Bloom-aligned objectives
  • Create: golden and adversarial evaluation datasets
  • Apply: labeling strategies and expected-output contracts
  • Analyze: dataset coverage and bias gaps
Topics
  • Dataset types:
    • golden set (representative tasks)
    • regression set (historical failures)
    • adversarial set (injection, ambiguity, edge cases)
    • load/performance set (high volume, long inputs)
  • Labeling strategies:
    • expected answer characteristics (not always exact text)
    • expected sources/citations for RAG
    • expected tool calls and sequence for agents
  • Coverage planning:
    • intent coverage, topic coverage, user personas, error-prone areas
  • Data governance:
    • handling PII, sanitization, retention, and audit constraints
Labs
  • Lab 2.1: Golden set build — Create a 50-item dataset with labeled intents, expected characteristics, and pass/fail checks.
  • Lab 2.2: Adversarial set build — Create an injection and tool misuse dataset; define safety expectations.
  • Lab 2.3: Coverage heatmap — Create a coverage matrix mapping dataset items to intents and risk areas.
Bloom-aligned objectives
  • Implement: automated evaluators for quality, groundedness, and safety
  • Design: scoring rubrics that are stable and repeatable
  • Evaluate: tradeoffs between precision and cost of evaluation
Topics
  • Evaluator types:
    • rule-based (schemas, citations present, length constraints)
    • model-graded (LLM-as-judge) with calibration
    • hybrid evaluators (rules + judge + heuristics)
  • Key scoring dimensions:
    • relevance, completeness, clarity
    • groundedness/faithfulness and citation correctness
    • safety/policy adherence, hallucination detection patterns (heuristics + judge)
  • Calibration and consistency:
    • reducing judge variability, evaluator prompts and rubrics
    • inter-rater agreement concepts
  • Reporting:
    • score distributions, failure clusters, examples
Labs
  • Lab 3.1: Citation validator — Implement checks for citation presence, validity, and mapping to retrieved chunks.
  • Lab 3.2: Groundedness evaluator — Implement a groundedness scoring rubric and evaluator for RAG answers.
  • Lab 3.3: Safety evaluator — Implement a policy compliance evaluator for unsafe content/tool misuse attempts.
  • Lab 3.4: Evaluator calibration drill — Run evaluators across a dataset, identify inconsistent scoring, and refine rubrics.
Bloom-aligned objectives
  • Evaluate: retrieval quality and grounding correctness
  • Analyze: root causes (chunking, indexing, ranking, filtering)
  • Create: targeted regression tests for retrieval failures
Topics
  • Retrieval evaluation:
    • hit rate, recall@k, MRR, NDCG (conceptual + applied)
    • metadata filter correctness and access boundary tests
  • Chunking and indexing tests:
    • chunk quality heuristics (length, overlap, metadata completeness)
    • duplicate and stale content detection
  • Grounding tests:
    • citation coverage, snippet correctness, “insufficient evidence” behavior
  • Drift and freshness:
    • content updates, re-indexing integrity, regression detection
Labs
  • Lab 4.1: Retrieval benchmark — Build a retrieval benchmark suite and compare configurations.
  • Lab 4.2: Access boundary test — Validate that unauthorized content is never retrieved under role/tenant filters.
  • Lab 4.3: “No answer” routing — Implement and test refusal behavior when evidence is weak or missing.
Bloom-aligned objectives
  • Implement: tests for tool selection, tool correctness, and side-effect safety
  • Evaluate: multi-step workflow success rates and recovery behavior
  • Analyze: agent failures (looping, wrong tool, partial completion, state drift)
Topics
  • Tool correctness testing:
    • schema validation for tool inputs
    • deterministic checks on tool outputs
    • timeout/retry/idempotency tests
  • Workflow evaluation:
    • task completion metrics
    • step correctness and sequence constraints
    • bounded autonomy (max steps, max tool calls)
  • Multi-agent handoffs:
    • role correctness, handoff quality, conflict resolution tests
  • Failure recovery:
    • fallback paths when tools fail
    • safe escalation to human review
Labs
  • Lab 5.1: Tool gateway simulator — Build a simulator that injects controlled failures (timeouts, partial data, rate limits).
  • Lab 5.2: Agent workflow test suite — Create end-to-end tests for 20 workflows with expected tool usage patterns.
  • Lab 5.3: Side-effect safety test — Validate idempotency and no duplicate actions under retries.
  • Lab 5.4: Handoff evaluation — Evaluate a supervisor/specialist setup on consistency and completeness.
Bloom-aligned objectives
  • Design: experiments that produce reliable decisions
  • Apply: A/B and canary strategies for LLM applications
  • Evaluate: improvements with statistical and practical significance
Topics
  • Offline vs online experiments:
    • when to trust offline evaluations
    • how to detect “offline-online” mismatch
  • A/B testing design:
    • success metrics, guardrail metrics (safety, latency, cost)
    • sampling and cohort considerations
  • Canary and shadow testing:
    • staged rollout, rollback triggers
  • Regression prevention:
    • mandatory gates for prompts, retrieval configs, tool schemas
Labs
  • Lab 6.1: A/B plan — Create an A/B experiment design with success and guardrail metrics.
  • Lab 6.2: Release gate policy — Implement a gating policy that blocks release if any guardrail metric regresses.
Bloom-aligned objectives
  • Implement: automated evaluation in CI/CD
  • Create: standardized evaluation reports and promotion criteria
  • Analyze: pipeline results and decide promotion vs rollback
Topics
  • CI pipeline design:
    • run evaluators on PRs and merges
    • publish evaluation artifacts
    • compare baseline vs candidate
  • Promotion criteria:
    • threshold-based and weighted scoring models
    • must-pass safety and security checks
  • Governance integration:
    • approvals for changes affecting safety or data boundaries
Labs
  • Lab 7.1: CI evaluation pipeline — Build an automated pipeline that runs evaluation suites and blocks merges on regressions.
  • Lab 7.2: Promotion report — Generate an executive-friendly evaluation report with failure analysis and recommendation.
Bloom-aligned objectives
  • Apply: continuous evaluation and drift detection
  • Analyze: production failures using trace-linked evidence
  • Create: incident playbooks and feedback loops
Topics
  • Continuous evaluation: sampling strategy, privacy considerations, cost controls
  • Drift detection: retrieval drift, prompt regressions, tool schema changes
  • Incident playbooks: quality drop, safety incident, tool outage
  • Feedback loops: labeling production failures into regression sets
Labs
  • Lab 8.1: Continuous evaluation config — Configure sampling-based continuous evaluation and dashboards.
  • Lab 8.2: Incident drill — Run a simulated “quality drop” incident; identify root cause and add regression tests.
Deliverable
A complete testing and evaluation package that includes:
  • Evaluation blueprint (metrics + thresholds + acceptance criteria)
  • Datasets (golden + adversarial + regression)
  • Evaluators (rules + judge + hybrid) with calibration notes
  • Automated CI pipeline with gating rules
  • A production monitoring and continuous evaluation plan
  • A failure taxonomy and remediation playbook
Tools and platforms used
  • Evaluation dataset formats and labeling templates (golden/regression/adversarial)
  • Automated evaluators (rule-based + model-graded + hybrid)
  • Tool gateway simulator for agent workflow testing
  • CI/CD integration for automated evaluation and promotion gates
  • Telemetry and monitoring dashboards for continuous evaluation and drift detection
Load More

Why Cognixia for This Course

  • Deep focus on evaluation and quality engineering for real-world GenAI and agentic systems
  • System-level testing approach that goes beyond model output checks
  • Enterprise-ready artifacts including datasets, evaluators, CI pipelines, and reports
  • Proven experience enabling safe, scalable GenAI adoption across industries

Mapped Official Learning

Testing & Evaluation of GenAI and Agentic Systems - Course @cognixiaTesting & Evaluation of GenAI and Agentic Systemsmastering-code-refactoring-and-debugging-with-ai-course@cognixiaMastering Code Refactoring and Debugging with AIgenerative-ai-testing-course@cognixiaGenerative AI Testingai-powered-code-generation-and-refactoring-course@cognixiaAI-Powered Code Generation and Refactoringsynthetic-data-generation-for-ai-course@cognixiaSynthetic Data Generation for AI
Explore Trainings

Designed for Immediate Organizational Impact

Includes real-world simulations, stakeholder tools, and influence models tailored for complex organizations.

Evaluation-Driven Engineering Testing and evaluation are embedded into the full GenAI lifecycle, from design through production.
High Hands-On Intensity Approximately 70% of the course is lab-driven, focused on building datasets, evaluators, and automation.
Enterprise QA Perspective Covers acceptance criteria, release gates, and governance expectations relevant to large organizations.
Continuous Quality Monitoring Extends testing into production with sampling-based evaluation and drift detection.

Let's Connect!

  • This field is for validation purposes and should be left unchanged.

Frequently Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

No. The course focuses on system-level testing applicable across models, platforms, and agent frameworks.
Yes. Dedicated modules address agent workflows, tool usage, handoffs, and failure recovery.
Yes. Automated evaluation pipelines and promotion gates are a core part of the course.
The course is highly practical, with hands-on labs, datasets, evaluators, and real testing scenarios.
Load More
×

Cognixia Special Offer

  • This field is for validation purposes and should be left unchanged.