Inspired By Frustration// Multi-agent fleets · MCP-native · production-shipped
AI Agent Development.
One operator, a working fleet. Multi-agent systems, MCP servers, and the harness around them — built by the engineer running ~55 merged PRs/day across roughly 30 parallel agents in production.
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.
AppHandoff
Production agent + MCP coordination layer
MCP Beast
Enterprise MCP proxy
55/day
Green-gated agent throughput
In short
AI agent development is the engineering of multi-agent fleets and production agents that survive real users — not single-prompt demos.
- Inspired by Frustration designs each agent and its MCP server as one system: self-describing tools, idempotent operations, retries, a golden-task eval harness, and structured observability, with rate limits, circuit breakers, and audit logging that make autonomous tool access safe.
- Receipts: ~55 merged PRs/day on average across roughly 30 parallel agents (peak 111 in a single day), each change auto-merged the moment its hard CI gate is green, on a self-hosted Fly runner fleet.
- The agent infrastructure we sell is the agent infrastructure we ship.
An AI agent development company builds and operates the whole system around a model — not just one prompt or one agent.
Most teams I talk to are stuck on one agent at a time.
We run a different shape: a fleet of roughly 30 parallel coding agents — Claude Code, Cursor, Codex, Copilot — coordinated through one MCP server (AppHandoff), gated by one CI rollup, and supervised by one human.
It merges ~55 PRs/day on average, peaks at 111, and lands every change on a green-only gate.
The architecture is what makes them honest: lane-claim coordination so agents do not collide, contracts so consumers do not drift, evals so quality does not regress, and observability so cost and latency do not surprise.
AppHandoff, MCP Beast, and the infra-gha-runners-fly fleet are the systems I built to do this and the systems I sell.
What we deliver
Custom AI Agent Builds
From brief to deployed agent: tool schema, prompt design, error handling, retries, observability. TypeScript-first, Anthropic and OpenAI friendly.
Agent + MCP Server Pairing
Design the MCP server and the agent that consumes it as a single system. Self-describing tools, idempotent operations, clean failure modes.
Agent Evaluation & Hardening
Build the eval harness that lets you ship changes without regressions: golden tasks, regression suites, structured logging, drift alerts.
Agent Cost Engineering
Reduce cost-per-task without breaking quality: model routing, prompt caching, batched calls, distillation. Real numbers, not hand-waving.
Existing Agent Rescue
Stuck agent project that hallucinates, loops, or burns tokens? Audit, identify the failure modes, and harden the harness. Most rescues take 1–3 weeks.
What backs these numbers
Production agent + MCP coordination layer
Real AI agents across Claude Code, Cursor, and Codex coordinate through it — JSON-RPC over MCP, a realtime kanban, pgvector commit-to-ticket classification, and auto-close on merge. Built and run by me.
Enterprise MCP proxy
Cuts always-on tool tokens from ~38,000 to ~400 for a 10-server org via three meta-tools and hybrid vector + keyword tool discovery. The agent infrastructure I sell is the infrastructure I ship.
Green-gated agent throughput
My own agent fleet (infra-gha-runners-fly) merges ~55 PRs/day on average behind a hard release gate — proof the harness, evals, and observability hold under real, continuous load.
Why us
Production Agent Track Record
Multiple agents in active production use — billing automation, content pipelines, MCP-driven coordination. Real users, real failure modes, real fixes.
MCP-Native by Default
Agents and MCP servers are designed together. Tool boundaries, auth, audit logging, structured errors — engineered as a single system.
Eval-First Mindset
Every agent ships with a regression harness. You can iterate the prompt or swap the model and know within minutes whether quality moved.
// 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
Ready to ship an agent that survives production?
Describe the workflow you want to automate and the constraint that's slowing you down. I'll tell you what's realistic, what the first version should look like, and what it'll cost.
Hire an AI agent developerFAQ
What does an AI agent development company actually do?
A real AI agent development company designs, builds, and operates the full system around a model — not just the prompt. That means tool schemas, idempotent operations, structured error handling, retries, an eval harness that catches regressions, and observability to debug what the agent actually did. Here that's not theory: AppHandoff (an MCP agent-coordination layer) and MCP Beast (an enterprise MCP proxy) are agents and infrastructure I built and run in production every day. The company you hire should be able to show you shipped agents under real load, not a demo.
How do I build an AI agent for my business?
Start by writing the workflow you want the agent to execute as a series of tool calls a junior employee could follow. That document becomes the tool schema. From there: pick a model with strong tool-use (Claude Sonnet 4 or GPT-5-class), wrap each tool with idempotent operations and structured errors, build a small eval harness with 20-50 golden tasks, and ship. Most production agents are 70% plumbing and 30% prompt — invest accordingly.
How much does it cost to build an AI agent?
A focused single-purpose agent (one workflow, 3-8 tools, MCP-native) typically takes 2-4 weeks at €8,000-€18,000. A multi-step business-process agent with auth, eval harness, and observability runs 4-8 weeks at €20,000-€55,000. Enterprise agents with governance, audit trails, and multi-agent orchestration take 8-16 weeks. Ongoing cost-per-task depends on model and traffic — for most production use cases, €0.02-€0.20 per completed task.
What makes a production AI agent different from a demo?
Demos are happy-path showcases; production agents have to survive real users, weird inputs, network failures, and partial tool errors. The difference shows up in five places: idempotent tool design, structured error responses (not opaque exceptions), retries with backoff, an eval harness that catches regressions before they ship, and observability that lets you debug what the agent actually did. Skipping any of these turns a working demo into an outage.
Should I use LangChain, LangGraph, the MCP SDK, or build from scratch?
For most production agents I now reach for the MCP SDK plus a thin TypeScript harness. LangChain is fine for prototyping but becomes friction at production scale. LangGraph is good when you genuinely need stateful multi-agent orchestration, which most teams don't. "From scratch" usually means a few hundred lines of TypeScript that wrap the model client — and is often the right answer for focused agents.
How do I evaluate an AI agent?
Build a golden-task suite: 20-50 task descriptions paired with expected outcomes (or expected tool-call sequences). Run it on every prompt change, every model change, every harness change. Augment with structured logging in production so you can mine real failure modes back into the eval set. Don't rely on "vibe testing" — agents fail subtly and the failures compound.