The Manifesto & Core Principles
Written in February 2001 by 17 developers at Snowbird, Utah, the Agile Manifesto established the values and principles now guiding 95% of organizations worldwide.
Snowbird, Utah
signatories
underpinning Agile
across all frameworks
PMI Agile Framework & Certification
The PMI-ACP is the fastest-growing Agile credential globally — ISO-accredited, experience-based, framework-agnostic. The March 2026 ECO covers four domains.
PMI Agile Practice Guide
The definitive PMI reference co-developed with the Agile Alliance. Covers Scrum, Kanban, Lean, XP, and hybrid approaches with situational guidelines. Free for PMI members. Essential exam prep and daily project reference for AI delivery teams.
Core Reference · Free for membersPMI Study Hall™
Digital learning platform with content-specific lessons, scenario-based practice questions, and domain-level performance analytics. Identifies knowledge gaps before exam day. Aligned directly to the March 2026 ECO.
Digital Prep ToolPMBOK® Guide 7th Edition
Shifted from process-based to principle-based guidance, explicitly incorporating Agile and hybrid approaches. Directly relevant for AI projects that must blend iterative delivery with governance requirements for legal and regulatory compliance.
PMBOK 7th Ed · Principle-basedPMI AI for Project Managers
PMI's dedicated AI learning program — the KICKOFF series covers AI capabilities, risks, and governance. PMI Infinity is an AI assistant for project teams. Essential before leading your first enterprise AI delivery program.
AI-Focused · Practitioner LearningApplying Agile to Generative AI
Traditional Agile was designed for deterministic software. Generative AI introduces non-deterministic outputs and probabilistic quality — requiring adapted ceremonies, definitions of done, and sprint structures.
Iterative Prompt Engineering
Prompt development maps naturally to sprint cycles. Each sprint produces a tested, versioned prompt with measured improvement against eval benchmarks. Backlog items become hypotheses with explicit success criteria.
Sprint CycleEvaluation-Driven Development
Replace unit tests with eval sets. Human evaluation, BERT Score, and benchmarks serve as the "tests" for GenAI output quality. Retrospectives review eval trends alongside velocity. Sprint demos include live model scoring.
Eval FrameworkCross-Functional AI Teams
Agile Principle 4 — daily business-developer collaboration — is critical for non-deterministic systems. GenAI teams need ML engineers, data scientists, domain experts, and product owners in the same standup.
Team StructureAI User Story Mapping
"As a customer service rep, I need the model to identify complaint categories so I can route tickets 40% faster." Acceptance criteria: precision ≥ 85%, latency ≤ 2s, hallucination rate ≤ 2% on evaluation set.
Product BacklogSpike Sprints for Research
Dedicated research sprints for RAG architecture experiments, fine-tuning approaches, and model selection. 2025 Springer research confirms LLMs can automate Agile reporting and requirement scoping — the AI Scrum Master pattern is production-ready.
Spike SprintAI-Augmented Agile
Claude and GPT can automate sprint reporting, draft acceptance criteria from feature descriptions, and surface velocity anomalies. LLMs reduce PM overhead on backlog refinement by 30–50% in mature AI delivery teams.
AI × Agile| Ceremony | Traditional Purpose | GenAI Adaptation | AI Tooling |
|---|---|---|---|
| Sprint Planning | Select stories; estimate velocity | Prioritize experiments by expected information gain; RAG vs fine-tune as explicit decision point; include GPU budget in capacity planning | Claude for story gen |
| Daily Standup | Block removal; progress update | Add "eval delta" — did model quality improve? Track GPU utilization and training cost burn rate alongside blockers | LLM standup summary |
| Sprint Review | Demo working software | Live model demo against eval set; present accuracy/latency metrics; stakeholder prompt-testing session with domain experts as validators | Eval dashboard |
| Retrospective | Team process improvement | Add "model behavior surprises" column; track hallucination patterns; review prompt versioning effectiveness; data quality retrospective | AI pattern analysis |
| Backlog Refinement | Clarify and estimate stories | Use LLM to draft acceptance criteria with measurable thresholds; assign spike vs delivery sprint; tag items by data dependency and compute budget | Claude for AC drafting |
Agile for Agentic AI
Agentic systems interact with external tools, APIs, and data sources to accomplish multi-step goals autonomously. Safety, oversight, and incremental trust-building are delivery requirements, not post-launch additions.
Tool Integration Sprints
Each external tool integration is its own sprint deliverable with explicit acceptance criteria: authentication, error handling, rate limiting, and audit logging all verified before sprint completion. Never add the next tool before the previous one is hardened.
Integration SprintSafety-First Backlog Priority
Human-in-the-loop controls, approval workflows, action reversibility, and prompt injection defenses are high-priority backlog items — never deferred post-launch. Agile Principle 9: continuous technical excellence applies to safety infrastructure first.
Safety SprintMinimal Viable Agent (MVA)
Adapt MVP to agentic systems: begin with a single-tool agent completing one workflow reliably. Expand tool access and task complexity in subsequent sprints. Never grant broad permissions before agent behavior is fully understood and evaluated in production conditions.
MVA PatternObservability as a Sprint Gate
Each agent action must be logged, explainable, and auditable. Trace logging, reasoning chain visibility, and action audit trails are required sprint deliverables — not post-launch add-ons — for enterprise deployments on Bedrock, Azure AI Foundry, or Vertex AI.
Observability GateOrchestrator–Worker Pattern
One orchestrator agent delegates to specialized workers. In sprint planning: orchestrator backlog governs overall workflow; each worker agent is a separate sprint deliverable with its own acceptance criteria and eval suite.
Parallel Agent A/B Testing
The spike sprint pattern accommodates parallel agent configurations: two approaches run simultaneously with identical eval suites to compare task completion rates, cost efficiency, and safety profile before committing to production architecture.
MCP Tool Registry
Model Context Protocol (MCP) standardizes tool access for agents. Each MCP server integration is a backlog item: authentication, error handling, rate limiting, and audit logging all verified before sprint completion. Claude's MCP ecosystem is the current production standard.
Choosing the Right Framework
No single Agile framework fits all AI/ML projects. The right choice depends on team size, complexity, organizational maturity, and whether you're building a product, an MLOps platform, or running an enterprise AI transformation.
MLflow — Experiment Tracking
Sprint-level experiment tracking: log parameters, metrics, and artifacts per sprint iteration. Integrates with Jira for traceability between sprint stories and model experiment results. Open source; available on all three major clouds.
↗ mlflow.orgAWS ML Well-Architected Lens
Sprint-ready checklists for model design, data management, governance, and deployment. Aligns cloud architecture reviews with Agile Definition of Done. An excellent sprint gate checklist for AWS-deployed AI programs.
↗ AWS ML LensDORA Metrics for MLOps
Google's DevOps Research metrics adapted for AI: deployment frequency, lead time for model changes, change failure rate, mean time to restore. Apply directly to MLOps pipeline health in Agile retrospectives.
↗ dora.devAgile Toolchain for AI Delivery
Practical platforms, certification pathways, project templates, and peer-reviewed research for Agile delivery of AI/ML programs across AWS, Azure, and Google Cloud.
AI Project Charter
PMI-aligned charter adapted for AI: model objective, success metrics (accuracy, latency, cost), data requirements, ethical considerations, compliance checkpoints, and rollback criteria.
PMI TemplateAI User Story Template
"As a [user], I need [AI capability] so that [outcome]." Acceptance criteria: accuracy ≥ X%, latency ≤ Y ms, hallucination rate ≤ Z%. Pairs BERT Score or human eval rubric as Definition of Done verification.
Story TemplateGenAI Retrospective Canvas
What did the model do well? / Where did it surprise us? / What prompt changes improved quality? / What data gaps blocked us? / What will we experiment with next sprint? Captures both team and model learnings.
Retro TemplateAI Sprint Velocity Dashboard
Modified velocity: completed stories + eval benchmark delta + GPU hours consumed + model version + hallucination incidents per sprint. Holistic health view beyond traditional story point velocity alone.
Metrics TemplateAI Product Roadmap
Quarterly roadmap: POC → Pilot → Production → Optimization. Includes data readiness gates, compliance checkpoints, model refresh cycles, and budget gates mapped to PMI milestone governance requirements.
Roadmap TemplateAI Ethics Sprint Checklist
Per-sprint gate: bias evaluation on protected attributes, privacy review of training data, transparency of model reasoning, content policy compliance, and responsible AI sign-off required before production release.
Ethics Gate