Abhay Jaiswal, CEO — Agilité
We deliver production software in weeks — not months — with 3-person teams. No sprints, no ceremonies, no knowledge transfer required. Proven across five engagements in five domains.
Four Levels of AI Maturity. Which One Are You On?
Level 1: AI Tools
Cursor, Copilot, ChatGPT. Basic tools to write basic code faster.
Level 2: AI-Assisted
More mature AI agents, advanced tooling, prompt engineering. Teams think they are AI-native. Better tools for faster code. Still stuck with process friction.
Level 3: AI Pods
Changed team structures, processes, and execution model. Full-stack developer concept. Eliminated redundant ceremonies. Build products much faster. Great for greenfield. Hits a wall on brownfield and complex systems.
Level 4: AI-Orchestrated
AI models coordinating against a continuously evolving understanding of your product, your business rules, and your history. Cognitive code validation. LLM-orchestrated testing. AI reasons about your system — not just your code. AI builds product. AI transforms complex legacy systems.
What Everyone Does vs. What We Do
Four key differentiators that separate Agilité from the common approach. These are not product names — they are how our engineers work on every commit, every engagement.
Analysis finds patterns. Cognition finds intent mismatches.
3 People Replace 10
An AI Pod is 3 people — one AI-native engineer, one QA, one PO. A single PO runs up to 5 pods. AI agents handle the roles that Scrum teams filled with specialists.
One Person. Three Tiers. Every Decision.
An AI-native engineer performs BA, PM, PO, Dev, and QA functions across the full delivery stack — what previously required 4-6 specialists.
Business Tier
- Requirements
- Prioritization
- Acceptance
- Domain understanding
Application Tier
- Architecture
- Implementation
- Testing
- Deployment
Data Tier
- Data modeling
- Data engineering
- Data flows
- Data pipelines
Quality is not a separate phase. It is part of the workflow itself.
The Knowledge Plane means any engineer picks up where another left off. Method-dependent, not person-dependent.
The Functions Survive. The Meetings Don't.
These ceremonies served a real purpose — creating shared understanding across large teams. We preserved the purpose and eliminated the overhead.
The Math
Traditional Scrum team of 7 people. 2-week sprint. 640 hours available.
Ceremony waste
35% of sprint capacity
QA overhead recovered
QA reduced from 33% to 20%
Total recovered
48% of sprint capacity
Add 2x AI developer speed on remaining capacity. Conservative result: 2-3x delivery speed. Not a claim — arithmetic.
Any Question About Your System — Answered in Seconds
All project context lives in a single layered markdown architecture — the Knowledge Plane. A continuously evolving understanding of your product, your business rules, and your history. Model-agnostic — any LLM can traverse it.
The architecture captures not just what the system is, but how it got here — every decision, every pivot, every lesson. AI agents keep this current as the system evolves.
Three Layers (organized by permanence)
CONTEXT.md — Single root index for every engineer and agent. New engineer reads the architecture. Full velocity within days. No onboarding. No tribal knowledge.
A Feature Shipped or It Didn't
No tasks, subtasks, story points, or velocity charts. Track features only. A feature is any vertical slice of working capability — product features, pipeline stages, infrastructure. Ship in days, not weeks. One feature, one pod, one owner. Quality is embedded during development through Cognitive Code Quality and Scenario Cognition — not bolted on after.
Ship in days, not weeks.
If it takes more than a week, split it.
Vertical slices only.
One pod per feature.
One feature, one pod, one owner.
No handoffs.
Build artifacts are the documentation.
Consistent Results.
Production systems serving real users in regulated and complex environments. Domain-agnostic and method-dependent.
Brownfield modernization
Est. 2 people/12 weeks. Delivered 1 person/4 weeks. Zero knowledge transfer.
Epic FHIR, PHI-compliant
Production-ready enterprise integration. 4 weeks.
Agentic AI product
Agentic invoice product, enterprise-grade. 4 weeks.
EPCIS Compliance Engine
Absorbed major pivot. 44 requirements. 8 weeks.
Greenfield RPM Platform, zero to production
Full platform, 3-person team. 12 weeks.
Five engagements. Five domains. Consistent results.
AI Generates Code. We Build Product. We transform complex systems.
6 Weeks
Fixed timeline. No open-ended engagement.
Outcome-Based
One real feature. Production-ready. Measurable result.
No Commitment Beyond the POC
Prove the model on your system. Then decide.
Start a POC Pod → aj@agilite.tech
Abhay
www.agilite.tech
The AI Pod Operating Model