Multi-agent workflow engine with Planner, Research, Coder, and Tester agents collaborating autonomously via LangGraph DAG — completing 156 complex software development workflows with 94% first-pass success rate and 5x developer velocity for a 40-engineer dev shop in Portland, OR.
NovaByte, a 40-engineer dev shop based in Portland, OR, was bottlenecked across every stage of their development pipeline. Code reviews took an average of 3 days to turn around. Bug investigation was entirely manual — senior engineers spent 6 hours on average tracing issues through a sprawling microservices codebase. Repetitive coding tasks like boilerplate generation, refactoring, and test writing ate directly into time that should have gone toward feature work.
Their VP of Engineering had evaluated autocomplete tools and copilot-style assistants, but none of them moved the needle. She needed AI that actually does work — not just suggests it. Something that could take a Jira ticket, understand the codebase, write the code, and validate it, end to end.
We built a multi-agent workflow engine where four specialized AI agents collaborate on complex software development tasks via a LangGraph-powered DAG. A Planner agent decomposes incoming tasks, delegates to Research, Coder, and Tester sub-agents, and orchestrates the full workflow autonomously — from Jira ticket to tested pull request, with human-in-the-loop checkpoints at critical stages.
Decomposes complex tasks into sub-tasks, assigns to specialized agents, manages execution order, and handles retries and escalation.
Searches the codebase, reads documentation, gathers context from PRs and issues, and builds a knowledge brief before any code changes.
Writes, edits, and refactors code following project conventions with full file system access and awareness of the team's style guide.
Runs existing tests, validates changes against regressions, generates new test cases, and reports coverage deltas to the Planner.
Directed acyclic graph execution with parallel branches, conditional logic, retry policies, and human-in-the-loop approval gates.
Persistent memory across sessions that learns codebase conventions, stores lessons from past workflows, and adapts agent behavior over time.
Built with cutting-edge AI orchestration infrastructure, deployed on the client's infrastructure with full data sovereignty and CI/CD integration.
I've been managing engineering teams for 12 years and I've seen every productivity tool under the sun. Most of them are glorified autocomplete. This is different. I watched the planner agent take a Jira ticket, break it into subtasks, have the research agent scan our entire codebase for context, send the spec to the coder agent, and pass the output to the tester — all in about 40 seconds. My senior devs were speechless. We're not replacing engineers — we're giving each one a team of AI assistants that actually understand our codebase. Routine PR turnaround went from 3 days to 4 hours. That alone is worth every penny.
Let's build an agentic AI system that supercharges your development team.
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