Enterprise delivery, modernization, and practical AI enablement for
distributed teams.
Agentic SDLC Tooling
Claude Code Agentic Plugin (Multi-Agent SDLC Orchestrator)
A multi-agent orchestration plugin for Claude Code designed to accelerate enterprise software
delivery through a structured SDLC pipeline. It is aimed at modernization work where parity, quality gates, and
review discipline matter as much as speed.
A Claude Code plugin that runs an agentic delivery pipeline across investigation, planning,
implementation, verification, pull request packaging, and review iteration.
A multi-agent system with focused roles rather than a single generalist assistant.
An artifact-driven workflow built for auditability, repeatability, and explicit quality control.
Core architecture
The design combines three layers that work together:
Knowledge: reusable packs covering source and target architecture patterns, testing
conventions, operational workflows, and onboarding material.
Workflows: orchestration definitions for migration pipelines, review passes,
release support, parity fixes, planning support, and self-improvement loops.
Agents: focused participants such as orchestrator, analyzer, planner, developer,
tester, scanner, review handler, and ruleset maintainer.
Delivery pipeline
Investigation produces an artifact covering intent, behavior, edge cases, and risks.
Planning produces an implementation artifact with steps, affected files, and test strategy.
Implementation produces a change manifest describing what changed and why.
Compliance checks verify ruleset adherence and block unsafe drift.
Quality scanning, targeted tests, and parity checks validate the change where applicable.
Review iteration processes feedback and applies focused fixes without widening scope.
Design principles
Pure orchestrator pattern: the coordinator delegates while specialists do the task-specific work.
Artifact-driven communication instead of ad hoc conversational handoffs.
Explicit quality gates, decision logs, and reviewable exceptions.
Debug and execution modes using the same workflow with different safety levels.
Persistent agent memory so institutional knowledge accumulates over time.
Integrations and use cases
Work item tracker integration for searching, updating, and linking artifacts to delivery items.
Git-based workflow support for branches, commits, pull requests, and review feedback handling.
Security, performance, and quality scanning as part of the gate sequence.
Particularly useful in legacy-to-modern API migration and other modernization backlogs that need
strong parity discipline.