Spec-Driven
Development.
Turn vague requirements into reliable AI-generated implementation using specs, tasks, tests, and review loops.
A practical 16-hour cohort program for software teams adopting AI coding agents like Claude Code, Codex-style agents, Cursor, GitHub Spec Kit, Kiro-style workflows, and Antigravity-style environments.
1. Capture Intent
Define user rules, objectives & outcomes
2. Write Spec
Lock down requirements, criteria & ADRs
3. Generate Plan
Establish codebase constraints & boundaries
4. Break into Tasks
Sequence logic into tiny, testable units
5. Implement with Agent
Let coding agents build step-by-step
6. Test & Review
Run assertions and verify PR changes
7. Release with Confidence
Merge code with complete traceability
AI coding is no longer limited by generation speed.
It is limited by clarity, context, and control.
Why Pay For This When Free SDD Content Exists?
An honest breakdown of free vendor content vs. the AIXL Live Cohort
It's a fair question — Microsoft Learn and AWS Skill Builder both have free modules on GitHub Spec Kit and Kiro, and DeepLearning.AI has a short free course on this topic. Here is the honest difference:
Free Self-Paced Content
Pre-recorded, tool-vendor tutorials teaching the basic mechanics of a single spec tool. Generic templates without code-level feedback or team integration context.
AIXL Live Cohort & Mentorship
Live, interactive cohort built by practitioners who have scaled this exact SDD workflow in enterprises like Walmart, EY, Hexaware, and Kotak Mahindra Bank. You bring your actual codebase/feature request and walk away with a graded spec pack, a runnable prototype, and live expert debugging.
* If you just want tool syntax, start with Microsoft Learn. Come here when you're ready to integrate SDD as an active team discipline with live feedback and reviews.
Why SDD Now?
AI coding agents can generate code quickly, but speed without specification creates hallucinated APIs, broken architecture, missed edge cases, weak tests, and unreviewable output.
Prompts are not requirements
A one-line prompt cannot capture business rules, edge cases, architecture constraints, and acceptance criteria. Agents need detailed definitions to succeed.
Fast code creates fast technical debt
AI can generate implementation quickly, but without a spec it can also generate inconsistent patterns, hidden assumptions, and fragile code.
Agents need context and constraints
AI coding agents perform dramatically better when they are given repository context, explicit constraints, task boundaries, and testable outcomes.
Teams need traceability
SDD connects business intent to specs, tasks, code, tests, reviews, and release decisions, keeping humans in control of development pipelines.
This is not prompt engineering.
Prompt engineering helps you ask better. Spec-Driven Development helps teams build better.
Prompt-Driven Coding
- Vague text prompts to start the agent
- AI generates massive amounts of unchecked code
- Developers manually debug and fix compilation failures
- Test coverage is added as an afterthought or omitted
- Context is lost between agent iteration cycles
- Extremely difficult for humans to review and verify
Spec-Driven Development
- Business intent is systematically scoped
- Requirements become machine-parseable specifications
- Specs are mapped to tasks and test cases first
- AI coding agents execute within closed context boundaries
- Developers verify outputs against explicit criteria contracts
- Implementation paths remain fully traceable and manageable
What is Spec-Driven Development?
Spec-Driven Development (SDD) is an AI-native software development approach where the specification becomes the source of truth before implementation begins. Instead of asking an AI agent to “build this feature,” the team first defines the problem, users, constraints, API contracts, task breakdown, and tests. The AI agent then works from a clearer contract, and the team reviews output against that contract.
Intent becomes specification
Ambiguous user ideas map to structured spec files.
Specification becomes plan
Architecture, data boundaries, and task sequences are logged.
Plan becomes implementation tasks
Granular directives are created for context-bound agents.
Tasks become code and tests
AI agents code exactly what is specified, guided by test assertions.
Reviews validate against intent
Humans review the code commits against the original criteria contract.
SDD Across Key Roles
Reduce trial-and-error, prevent rogue code drift, and provide agents with accurate repository context.
Validate requirements and turn user behavior criteria into automated tests before code generation.
Format user stories and business logic criteria into checklists directly consumable by AI tools.
Secure system structure, database schemas, and interface boundaries against agent drift.
Achieve complete project traceability, strict governance, and predictable coding velocity.
The SDD Operating Model
A repeatable, governance-focused lifecycle built for software teams executing with AI coding agents.
Interactive Operating Model
Move your cursor over the numbered stages of the orbital to inspect the core directives and objectives of the Spec-Driven Development cycle.
What You Will Actually Create
You will leave with reusable artifacts your team can immediately apply in AI-assisted development.
Feature Specification Pack
Acceptance Criteria Matrix
Architecture Constraint Sheet
Agent Task Breakdown
Test and Review Matrix
SDD Team Adoption Playbook
Artifact-led Outcomes
This cohort is designed around practical outputs, not only concepts. Leave with a portfolio of complete specifications.
Curriculum Overview
16 hours of structured learning, templates, labs, and capstone work.
Understand why standard autocomplete fails at scale and why coding agents require spec-driven boundaries to succeed.
- From autocomplete to autonomous coding agents (Claude Code, Cursor, Codex, Antigravity)
- Why vague prompts fail: hallucinated APIs, wrong database architectures, missed boundary checks
- Analyzing common AI code failure modes and debugging structural technical debt
- Introduction to SDD: where it fits in the modern AI-native software delivery lifecycle
- Roles alignment: connecting BAs, developers, architects, QA, and Scrum Masters
Deconstruct an AI-generated implementation failure and pinpoint missing requirements/constraints.
Hands-on Labs
Implement step-by-step specifications inside a playground environment, engineering contexts, ADRs, test matrices, and instructions.
Vague Request to Feature Spec
Turn simple verbal or text feature requests into machine-parseable specifications.
Story & Acceptance Criteria Builder
Draft robust Given/When/Then scenarios mapping critical edge flows.
Architecture Constraint Sheet
Lock down schemas and API endpoints preventing model context drift.
Agent Task Breakdown
Sequence logic into tiny, deterministic tasks suitable for autonomous agents.
Implementation Prompt Pack
Create robust instruction templates with system context rules.
Test Case Matrix
Author comprehensive validation checks verifying execution behavior.
AI Code Review Checklist
Define rules matching agent commits to original specification tasks.
Release Readiness Checklist
Run compilation, syntax, linting, and coverage checks for builds.
SDD Team Adoption Playbook
Design a 30-day team roadmap rolling out spec-driven workflows.
From Vague Feature Request to Agent-Ready Specification
Participants take a vague software feature request and convert it into a complete SDD package that can be used by an AI coding agent or software team.
Platform & Tool Stack
Tool-agnostic SDD workflow designed for modern AI coding environments.
AI Models & Assistants
AI Coding Agents
Specification & SDLC
Engineering Workflow
Governance
Built for AI-Native SDLC
For teams using AI coding agents
Create structure around agents so they work from clear requirements, constraints, and tests.
For existing Agile/Scrum teams
SDD strengthens backlog items, acceptance criteria, reviews, and Definition of Done.
For enterprise engineering
Add traceability, governance, and review discipline to AI-generated code.
For reusable delivery playbooks
Create templates your team can reuse across features and projects.
Corporate Deployment Options
For companies adopting AI-assisted development across teams.
Public cohort seats
Enroll developers in scheduled public cohorts with hands-on practice.
Private corporate cohort
Dedicated session mapped around internal constraints and repositories.
SDD templates customized for your SDLC
Tailored feature spec forms and checklists matching your code architectures.
Jira/GitHub workflow alignment
Mapping specs to issues, planning milestones, and setting Definition of Done checks.
AI coding agent governance playbook
Setting security guidelines, review standards, and compliance checkpoints.
Team capstone based on internal use case
Structure Capstones to build features for your actual codebase.
Who Should Attend
Software Developers
Developers using AI who want to transition from prompt trial-and-error to systematic context boundaries and specification-first implementation.
Tech Leads
Leads tasked with managing AI code velocity, preserving architectural constraints, and running predictable development sprints.
Solution Architects
Architects who want to learn how to design schemas, API boundaries, and system constraints so agents cannot drift from plan.
QA / Automation
QA professionals who want to translate business intent into automated tests that verify and check agent output.
Business Analysts
Analysts who want to generate specifications and Definition of Done checklists that are immediately ready for AI consumption.
Scrum Masters
Scrum Masters who want to adapt backlog grooming and sprint planning to accommodate AI agent velocity and human review loops.
Engineering Managers
Managers who need to enforce governance, traceability, and review standards across repositories modified by AI agents.
Delivery Managers
Managers who need to track scope and predictably deliver software utilizing coding agents without introducing fast technical debt.
Teams using or planning to use AI coding agents for software delivery.
- Basic SDLC understanding
- Ability to read software requirements
- Basic Git awareness
- Familiarity with user stories or Agile
- Basic understanding of APIs or app dev
- Advanced programming
- Machine learning
- Prior experience with GitHub Spec Kit
- Prior experience with Kiro
- Deep cloud knowledge
Program Format & Pricing
Launch Individual Enrollment
₹12,000 + GST
- 16 live instructor-led hours
- 6 structured modules
- 8 reusable SDD artifacts
- Capstone review pack
- Templates and checklists
- AIXL Academy certificate
- Optional FDE bundle: complete pathway for ₹39,999 + GST total (save ₹11,000)
Launch cohort pricing is available for the first public cohort only.
Corporate Batch Scopes
Custom Pricing
- Private batch for engineering teams
- Custom SDLC templates customized for your stack
- Jira/GitHub workflow mapping integration
- Internal use case capstone project selection
- AI coding governance playbook custom review
- Team adoption roadmap setup
Custom quotes generated based on team size and tailoring needs.
Simple Cohort Admission Process
Register Interest
Submit the short interest form on this page.
Fitment Review
Our team verifies role alignment.
Confirm Seat
Complete payment to secure batch slot.
Pre-work Pack
Receive templates & instructions early.
Join Cohort
Begin the 16-hour live cohort sessions.
Enrollment FAQ
No. Developers, QA engineers, business analysts, Scrum Masters, architects, tech leads, and engineering managers can benefit because SDD improves how teams define, build, test, and review software with AI agents.
No. Agile user stories are one input. SDD goes further by connecting business intent, specifications, architecture constraints, implementation tasks, tests, code reviews, and release readiness.
No. Prompt engineering focuses on asking better questions. SDD focuses on creating a repeatable engineering workflow where specs, tasks, tests, and reviews guide AI-assisted software development.
No. The workshop introduces the concepts and shows how GitHub Spec Kit-style and Kiro-style workflows can be applied. Prior experience is not required.
The workshop is not a beginner programming course. It focuses on creating high-quality specifications, agent tasks, prompts, tests, and review workflows. Some examples may use code or repositories for context.
The workshop discusses workflows that can be used with Claude Code, Codex-style agents, Cursor, GitHub Spec Kit, Kiro-style environments, and Antigravity-style agentic IDEs.
Yes. The corporate version can be customized around the client’s SDLC, Agile templates, Jira/GitHub workflow, architecture standards, compliance needs, and internal AI coding tools.
SDD is a core skill inside Forward Deployed AI Engineering. FDEs use SDD to convert ambiguous business problems into buildable, testable, AI-agent-ready specifications.
Yes. SDD does not replace Scrum. It strengthens the quality of backlog items, acceptance criteria, implementation tasks, testing, code reviews, and AI-agent execution.
Yes. Participants receive reusable templates for feature specs, acceptance criteria, task breakdown, agent instructions, test matrix, code review checklist, and release readiness.
Stop prompting.
Start specifying.
Join the launch cohort and learn the operating model for reliable AI-assisted software development.
Launch pricing: ₹12,000 + GST. Limited to the first public cohort.