GitOps Multi-Agent SDLC Automation Platform One-Pager

Internal Product

Principal Architect / AI Systems Architect / Agentic SDLC Engineer

Context

Designed and implemented a GitOps-oriented multi-agent SDLC automation platform where AI agents analyze repositories, propose fixes, remediate bugs, generate patches, validate outputs, score acceptance criteria, preserve audit trails, and route work through Git-based review workflows instead of opaque one-off chat sessions.

Problem

AI coding tools often produce useful snippets but weak engineering process. They lack durable task state, issue linkage, acceptance criteria, regression checks, review gates, ownership, and audit evidence. For real software delivery, agents need to work inside the SDLC: issues, branches, commits, tests, reviews, security scans, evaluation scores, and traceable decisions.

What I did

• - Designed Git as the source of truth for agentic SDLC work, including issues, branches, commits, merge requests, reviews, remediation records, and audit trails.
• - Defined agent workflows for repository analysis, bug triage, task decomposition, patch generation, code review, test planning, security scanning, and documentation updates.
• - Used workflow-graph thinking inspired by LangGraph, CrewAI, and GitOps patterns to make agent actions explicit rather than hidden inside chat.
• - Created acceptance-criteria models so agents could judge whether generated work satisfied the original task before presenting it for review.
• - Designed evaluator agents to score generated patches, identify regressions, compare outputs against expected behavior, and flag weak or unsupported changes.
• - Integrated security review concepts such as SAST, dependency checks, secrets scanning, prompt-injection-aware tool use, and unsafe-code pattern detection.
• - Connected agent activity to GitLab-style workflows for bugs, remediation tasks, service tickets, commits, merge requests, and audit evidence.
• - Designed trail-of-work records so every agent suggestion, patch, decision, failure, retry, and acceptance score could be reconstructed.
• - Separated generation agents from reviewer/evaluator agents to reduce blind trust in a single model output.
• - Used schema-defined prompts, typed task objects, structured outputs, and explicit JSON contracts to reduce ambiguity.
• - Designed model benchmarking and routing so different local or hosted models could be selected for different SDLC tasks.
• - Aligned the operating model with ISO 42001-style AI management expectations and OWASP LLM / agentic application security principles.
• - Designed a migration path from brittle n8n and AutoGPT-style automations toward more governable Flowise and Sim Studio workflows, backed by a custom workflow DSL and containerized cron-based converters.
• - Defined LLM skills as explicit, reusable units with typed input/output contracts, constraints, examples, and failure modes — making prompts and skills versioned software assets.
• - Implemented acceptance-gated workflow promotion using evaluator agents and AI-assisted bug-fixing agents to reduce manual review burden.

Outcome

• - Created a practical architecture for agentic software delivery grounded in Git, issues, branches, commits, reviews, and evidence.
• - Moved AI coding assistance from one-off chat output toward traceable SDLC automation.
• - Established evaluator-agent and acceptance-criteria patterns for reducing unreviewed or low-quality code generation.
• - Improved governance by preserving a trail of work and linking agent actions to bugs, remediation tasks, tests, and review artifacts.
• - Created a reusable control model for AI-assisted bug remediation, service work, refactoring, documentation, and security improvements.
• - Connected multi-agent workflows to AI product-security concerns including excessive agency, tool scoping, output validation, logging, and auditability.
• - Produced a portfolio case demonstrating hands-on agentic SDLC architecture rather than generic AI coding-assistant usage.

Evidence

• - Designed around Git-backed work records including issues, branches, commits, merge requests, reviews, acceptance criteria, and audit trails.
• - Supports multiple agent roles including repository analyst, planner, patch generator, reviewer, evaluator, security scanner, documentation agent, and acceptance judge.
• - Connects generated work to GitLab-style remediation workflows and Mattermost-style collaboration surfaces.
• - Uses structured task objects, JSON output contracts, evaluator scoring, and explicit review gates.
• - Public-safe metric language; private repositories, client names, prompts, model scores, code patches, issues, and proprietary workflow details are omitted.