The Agentic SDLC
How we build software that's faster, better, and built to last.
Orchestrated AI agents across every phase of delivery. Not tools bolted onto a process. A connected system where each phase feeds the next, governed end-to-end by elite engineers and continuously monitored by a dedicated team that improves the system with every engagement.
The Difference
Every vendor has tools. We have a system.
Most teams use AI the same way they use Google: ad hoc, one question at a time. An AI writes some code, a developer checks it. That's a tool. That's not a system.
The Agentic SDLC (ADLC) is an orchestrated workflow where agents are embedded at every phase, from requirements through deployment, and the output of each phase feeds cleanly into the next. What makes it durable is governance: agents run within defined boundaries, under senior engineer review, with architecture documentation required before any code is written.
Connected Phases
Requirements surface in architecture. Architecture informs the build. Build feeds test. Test gates deploy. Every artifact flows forward. Nothing is recreated from scratch.
Orchestrated, Not Incidental
Agents are designed into the process at the start, not discovered mid-project. Each agent has defined inputs, defined outputs, and defined governance.
35 Years Encoded
Our institutional knowledge, patterns, anti-patterns, and playbooks are embedded into the agents. They benefit from 35 years of delivery history, not just training data.
Always On
A team that watches the system and makes it better.
Agents execute the work. A dedicated ADLC operations team makes sure the system stays sharp: monitoring every engagement, tuning what agents do, and encoding lessons back into prompts, playbooks, eval datasets, and governance standards. The ADLC is not static. It improves with every delivery.
Dedicated ADLC operations team
Senior engineers sit above every engagement, watching how agents perform in practice: output quality, handoff friction, eval pass rates, and governance compliance.
Continuous monitoring
Delivery Intelligence Agents ingest live signals from GitHub, Jira, CI/CD, and Slack. The operations team interprets what those signals mean and acts before small issues become delivery debt.
System-wide improvement
Every lesson feeds back into the ADLC itself: updated agent prompts, refreshed eval datasets, sharper playbooks, and tighter guardrails. The system gets better with every project we run.
The Five Phases
The Agentic SDLC in full
Each phase has dedicated agents, a governing engineer, defined outputs, and a handoff contract into the next phase.
Requirements
Interactive POC in days. Agentic use cases surface here.
Requirements AgentArchitecture
Docs required before any code. Built for a 10-year horizon.
Architecture AgentBuild
60–80% AI-assisted. 100% senior-reviewed. No exceptions.
Build AgentTest
Continuous QA agents. SDET governs evaluation datasets.
QA AgentDeploy
CI/CD agents ship it. Delivery Intelligence monitors every engagement.
Deploy AgentPhase 1
Requirements
The requirements agent accelerates discovery by mapping business goals to technical scope, identifying agentic opportunities early, and generating an interactive proof-of-concept within days, not weeks.
Agent Does
Synthesizes stakeholder inputs, generates use case maps, identifies automation candidates
Engineer Governs
Business logic validation, scope prioritization, POC sign-off
Output
Requirements doc, agentic opportunity map, interactive POC
Phase 2
Architecture
No code is written before architecture is documented. The architecture agent generates system design drafts informed by 35 years of patterns, then a senior architect reviews against a 10-year horizon: integrations, scale, maintainability.
Agent Does
Generates design alternatives, highlights integration risks, documents decisions
Engineer Governs
Technology selection, risk acceptance, architecture sign-off
Output
Architecture decision records, system diagrams, integration contracts
Phase 3
Build
60–80% of code is AI-assisted. Every line is reviewed by a senior engineer. We are explicitly anti-vibe-coding: agents accelerate output but do not eliminate accountability. Senior review catches what agents miss.
Agent Does
Code generation, boilerplate elimination, context-aware suggestion from architecture docs
Engineer Governs
100% code review, security patterns, performance and edge case validation
Output
Reviewed, tested, version-controlled software with full traceability
Phase 4
Test
QA agents run continuously, not just before release. They generate test cases from requirements, flag regressions in real-time, and surface flakiness patterns. An SDET governs all evaluation datasets to ensure agents are testing the right things.
Agent Does
Test generation, continuous regression, flakiness detection, coverage analysis
Engineer Governs
Eval dataset design, test plan approval, acceptance criteria validation
Output
Test reports, coverage artifacts, regression history, release readiness signal
Phase 5
Deploy
CI/CD agents orchestrate deployments. Once in production, Delivery Intelligence Agents monitor every engagement in real-time, surfacing issues before they become incidents and routing insights to the right people.
Agent Does
Pipeline orchestration, deployment validation, post-deploy health monitoring, incident triage
Engineer Governs
Release approval, rollback decisions, escalation authority
Output
Production-ready deployments, health dashboards, incident reports with root cause
Delivery Intelligence
We see problems before you do.
Every active engagement is monitored by Delivery Intelligence Agents. They ingest GitHub commits, Jira tickets, CI/CD pipeline signals, and Slack communications in real-time, and they act on what they find.
These are not passive dashboards. When a blocker surfaces, the agent diagnoses the root cause, assesses downstream impact, and routes the right insight to the right person before anyone has to run a standup. Behind the agents, our ADLC operations team reviews what surfaces, decides what to change, and pushes improvements back into the system.
Real-Time Signal Ingestion
GitHub, Jira, CI/CD pipelines, and Slack are monitored continuously. Not sampled. Not summarized after the fact.
Root Cause Diagnosis
Agents surface blockers with context: what caused it, what it affects, and what the historical analogs are from past engagements.
Role-Specific Routing
Engineers get technical signals. PMs get schedule risk. Executives get executive summaries. The right insight reaches the right person automatically.
35-Year Pattern Library
When a risk pattern matches something from our delivery history, the agent flags it along with how we resolved it before.
The Pod Model
The Core Pod on Every Engagement
A lean, senior, AI-augmented unit, a human/agent operating cell where every person governs a specific domain of the Agentic SDLC.
Product Owner
Owns: Outcomes and agentification decisions
Defines what gets built and which workflows get agentified. Validates POC before the enterprise build begins.
Lead Developer
Owns: Engineering execution and AI orchestration
Orchestrates AI agents during the build phase. Reviews all AI-generated code. Ensures structured outputs meet architectural standards.
Principal Developer
Owns: Technical standards and architecture integrity
Sets the technical bar. Owns architecture docs before any code is written. Runs quarterly debug drills. The safety net for speed.
Lead SDET
Owns: Quality, evaluation, and release confidence
Owns the entire evaluation layer. Designs eval datasets, behavioral tests, and release confidence gates. More powerful, not replaced, in an AI-first pod.
On-demand specialists, integrated into the pod
Every engagement draws on shared specialist capability as the work demands. Every specialist operates under the same ADLC standards and governance.
Architect
Expert-level AI governance across all domains. Engaged for complex agentic system design: multi-agent orchestration, LLM routing strategy, and human-in-the-loop design.
Data Engineer
Builds the knowledge infrastructure agents run on: RAG pipelines, vector stores, and enterprise knowledge bases.
ML Platform Engineer
Handles model adaptation: fine-tuning, multi-modal pipelines, and domain-specific optimization.
DevOps
Owns the ADLC runtime: runners, CI/CD hooks, deployment environments, and rollback paths.
SysOps
Manages MCP servers, observability, service health, and secrets across all engagements.
Pod Skill Coverage
Governance
AI without governance creates debt at speed.
The fastest AI-generated code in the world is worthless if no one can maintain it, audit it, or trust it in production. Our ADLC has four non-negotiables that apply to every engagement, every time.
Architecture docs before code
No build phase starts without an approved architecture document. This is the single most effective way to prevent AI-generated code from becoming architectural debt.
Senior review on all AI-generated code
Every line of AI-generated code is reviewed by a senior engineer. Not spot-checked. Not reviewed when time allows. Always. Without exception.
Quarterly debug drills
Teams run structured debugging exercises quarterly, not just when something breaks. Engineers must be able to understand, trace, and repair AI-generated code under pressure.
Product owners qualified to evaluate AI output
Our POs are trained to assess what AI agents produce: not just whether features work, but whether agent behavior is correct, safe, and aligned with defined boundaries.
Ready to see the ADLC in action?
We can show you the system, including a working agent, in a single discovery session.