Inspired By Frustration// AppHandoff · MCP · self-hosted CI fleet
Agent Orchestration.
One operator, a swarm of AI coding agents, a hard release gate. I design and run multi-agent systems that ship 50+ production PRs a day without losing tests, review, or control — and build the same capability for your team.
Why teams hire us
Senior engineering judgment, applied where it ships value.
Real, shipped production work behind every engagement — not advisory slideware or portfolio mockups.
55/day
merged PRs (avg)
~30
parallel agents
green-only
release gate
In short
Agent orchestration is the engineering that lets many AI coding agents work as a coordinated team instead of colliding — work division into lanes, a shared coordination contract (an MCP server plus a single main branch), evaluation of agent output, and a green-only release gate.
- Inspired by Frustration runs this in production daily: ~55 merged PRs/day on average (peak 111 in a single day) across roughly 30 parallel agents, each change landed by GitHub-native auto-merge the moment its CI gate is green, on a self-hosted CI/CD fleet.
- The coordination layer is its own product, AppHandoff — JSON-RPC over MCP, a realtime kanban of lanes and tickets, pgvector auto-classification of commits to tickets, and auto-close on merge.
Most 'AI agents' are a single prompt in a loop.
Orchestration is the engineering around the swarm: how agents divide work without colliding, how they coordinate through a shared contract, how their output is evaluated instead of trusted, and how nothing ships until CI, tests, and deploy checks are all green.
I run this on my own infrastructure every day — ~55 merged PRs/day on average (peak 111) across roughly 30 parallel agents, every change auto-merged on a green CI gate — and I build it for teams who want the throughput without the chaos.
What we deliver
Swarm & Coordination Design
How many agents, how they split work, and the coordination layer that stops them colliding. Lane/ticket systems over a shared main branch, MCP-based handoff, and contracts agents can't drift from.
MCP Coordination Layer
An MCP server your agents share as the source of truth — tickets, stages, auto-classification of commits to work items, and auto-close on merge. Built on the same design as AppHandoff, my own coordination product.
Eval Harnesses & Guardrails
Agent output is checked, not trusted: golden-task suites, regression gates, structured logging, and rate limits plus circuit breakers so a misbehaving agent can't take down a system.
Guarded Release Pipelines
A green-only release gate: code ships only when CI, end-to-end tests, and deploy checks all pass. Drain-aware deploys that never kill an in-flight job, plus an autonomous CI auto-fixer that diagnoses failures and opens the fix PR.
Self-Hosted Agent CI Fleet
The infrastructure that makes 100+ PRs/day affordable: a self-hosted runner fleet with shared composite actions, content-addressable caches, and an 8-minute job budget — tuned for parallel agents, not a single human.
What backs these numbers
merged PRs (avg)
30-day average; peak 111 in a single day
parallel agents
running concurrently on a self-hosted fleet
release gate
ships only when CI, tests, and deploy checks pass
Why us
Proven at ~55 PRs/day
Not a slide — a measured baseline. ~55 merged PRs/day average over 30 days, peak 111 in a single day, ~30 agents in parallel, every change auto-merged on a green-only gate. The system runs this site, AppHandoff, MCP Beast, and the fleet itself.
I Built the Coordination Layer
AppHandoff is my own MCP coordination platform: JSON-RPC over MCP, a realtime kanban of lanes and tickets, pgvector auto-classification of commits to tickets, and auto-close on merge. The orchestration I sell is the orchestration I run.
Governed, Not Feral
Every agent tool call is rate-limited (30/min), circuit-broken (5 failures opens the breaker), and audit-logged. Agents get real access to real systems — with the controls that make that safe in production.
// what clients say
Proof from shipped work.
We came in with a Lovable prototype and a board deadline. Three weeks later we had a typed backend, real auth, and an MCP server our support agents actually trust. The POC went to production without the usual rewrite tax.
DaanHead of Engineering
fintech scale-upPOC → production
I needed someone who could orchestrate a swarm of coding agents and still own the architecture. The agent-orchestration setup shipped 40+ PRs in a week — every one reviewed, scoped, and reversible. No hallucinated mess to clean up.
M.R.Founder
B2B SaaSagent orchestration at scale
The MCP integration was the part three other vendors quoted us six months for. Here it was live in under three weeks — tool schema, OAuth, rate limits, traces, the lot. Our Claude agents finally touch real data safely.
PriyaVP Product
healthtech startupMCP integration
Want your team shipping at agent speed — safely?
Tell me what you're building and where the bottleneck is. I'll tell you what an agent swarm can realistically own, what the coordination layer and release gate should look like, and what it costs to stand up.
Get in touchFAQ
What is AI agent orchestration?
Agent orchestration is the engineering that lets multiple AI coding agents work as a coordinated team instead of stepping on each other. It covers how work is divided into lanes, how agents coordinate through a shared contract (usually an MCP server plus a single main branch), how their output is evaluated, and how a release gate keeps anything from shipping until CI, tests, and deploy checks are all green. The agents do the typing; the orchestration makes the result trustworthy.
How many PRs a day can a swarm of agents actually ship?
On my own infrastructure: ~55 merged PRs/day on average over a 30-day window, with a peak of 111 in a single day, across roughly 30 agents running in parallel, with every change auto-merged the moment its CI gate is green. The ceiling isn't the agents — it's how fast and reliable your CI, caches, and release gate are. That's where most of the engineering goes.
How do agents avoid colliding on the same code?
Each agent does isolated work in its own branch or worktree and only merges to main once it's green — so half-finished work is never visible to the others. A shared coordination layer (an MCP server with tickets and lanes) assigns work and tracks state, and commits are auto-classified back to the right ticket. The single meeting point is the main branch; everything else stays isolated.
Is letting AI agents ship to production safe?
Only with a gate. The rule I hold: nothing publishes until CI, end-to-end tests, and deploy checks are all green — agents keep working until they are. Every tool call an agent makes is rate-limited, circuit-broken, and audit-logged, so an agent can't run away with your systems. Safe agent orchestration is mostly about the guardrails, not the model.
Can you set this up for my team, or only run it yourself?
Both. I run this system daily on my own products, and I build the same capability for teams: the coordination layer (often an MCP server), the eval harness, the self-hosted CI fleet tuned for parallel agents, and the guarded release pipeline. Most teams start with one workflow and one or two agents, then scale the swarm as the gate proves itself.