Inspired By Frustration// Agents · MCP · workflows wired to real systems
AI Automation Agency.
Not another brittle Zapier chain. I build AI automation that's wired into your real systems — agents and workflows with auth, rate limits, audit trails, and a release gate — by an operator who runs automated systems in production every day.
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
Green-gated automated PRs
AppHandoff
MCP coordination layer I built
MCP Beast
Enterprise MCP proxy I run
In short
Inspired by Frustration is an AI automation studio that wires agents and workflows into your real systems — CRM, billing, docs, inbox — instead of brittle Zapier chains.
- It is run by an engineer who operates automated multi-agent systems in production every day, including an MCP coordination layer (AppHandoff) and an enterprise MCP proxy (MCP Beast), and whose own fleet ships 50+ production PRs/day.
- Every automated action is rate-limited, circuit-broken, and audit-logged behind a green-only release gate, so automation that touches your business systems is governed like production code — not a black box.
An AI automation agency that runs its own automation in production — not a no-code reseller.
Most 'AI automation' breaks the first time reality changes a field name.
Real automation is engineering: an agent or workflow that talks to your actual data and tools, handles failure, logs what it did, and can be trusted to run unattended.
I run automated multi-agent systems daily — they merge ~55 production PRs a day behind a green-only release gate on my own infrastructure — and I build the same kind of dependable automation for teams who are tired of toys.
What we deliver
Workflow & Process Automation
Map the repetitive workflow, then automate it end-to-end: triggers, steps, error handling, and human checkpoints where they matter. Wired to your CRM, billing, docs, and inbox — not a sandbox.
Agent-Driven Automation
When rules aren't enough, an AI agent that reasons over your tools: classification, drafting, triage, research, and multi-step tasks. Tool boundaries, retries, and evals so it stays reliable.
Systems Integration & MCP
Connect the things that don't talk to each other. Typed integrations and Model Context Protocol servers so your agents and automations reach every system through one governed, auditable layer.
Governed & Observable
Every automated action is rate-limited, circuit-broken, and audit-logged. You get visibility into what ran, what it changed, and what to do when something looks off — production controls, not a black box.
Automation Rescue
Inherited a pile of half-working Zaps, scripts, and 'AI' that nobody trusts? Audit it, find the failure modes, and rebuild the critical paths on a foundation that holds. Most rescues take 1–3 weeks.
What backs these numbers
Green-gated automated PRs
My own agent fleet (infra-gha-runners-fly) merges ~55 PRs/day on average behind a hard release gate — unattended automation proven under continuous, real load.
MCP coordination layer I built
A live Model Context Protocol platform that wires AI agents to real systems with auth, rate limits, circuit breakers, and per-call audit logging — the exact controls your automation needs.
Enterprise MCP proxy I run
Governed, audited access from agents to many systems through one layer — proof that connecting automation to real tools safely is the core of the work here, not an afterthought.
Why us
I Run Automation in Production
This isn't theory. I operate automated multi-agent systems daily — billing automation, content pipelines, an MCP coordination layer, and a self-hosted CI fleet shipping 50+ PRs a day. The automation I sell is the automation I depend on.
Built for Real Systems
I built AppHandoff (an MCP coordination platform) and MCP Beast (an enterprise MCP proxy with governance and audit). Connecting agents to real tools safely is the core of the work, not an afterthought.
Safe by Design
Rate limits, circuit breakers, audit logs, and a green-only release gate. Automation that touches your systems should be governed like any production code — and here, it is.
// 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
What would you automate if it actually worked?
Tell me the workflow that's eating your week and the systems it touches. I'll tell you whether it's a rule, an agent, or both — what it takes to wire it safely, and what it costs.
Hire an AI automation agencyFAQ
What does an AI automation agency actually do?
An AI automation agency designs and builds systems that do repetitive or judgment-heavy work for you — wired into the tools you already use. That ranges from rule-based workflow automation (triggers and steps across your CRM, billing, and inbox) to AI agents that reason over your data for classification, drafting, triage, and multi-step tasks. The job is the engineering around it: integrations, error handling, evaluation, and the controls that make it safe to run unattended.
What can I actually automate with AI?
Good candidates are workflows that are repetitive, rule-heavy, or bottlenecked on a person reading and routing information: invoice and document processing, support triage and first-draft replies, data entry and enrichment, content pipelines, reporting, and internal coordination. If a careful junior could do it from a written procedure, it's usually automatable. If it needs real judgment or live access to systems, that's where an agent (not just a workflow) earns its keep.
How much does AI automation cost?
A focused single workflow (one process, a few integrations) typically lands at €6,000–€16,000 and 1–3 weeks. A multi-step agent automation with auth, evals, and observability runs €18,000–€50,000 over 4–8 weeks. Ongoing run-cost depends on volume and model — for most automations it's cents per task. The expensive mistake is the cheap automation that silently breaks; the controls are what you're really paying for.
How is this different from Zapier or Make?
Zapier and Make are great for simple, stable, low-stakes connections — and I'll tell you when that's all you need. They get brittle when the logic is conditional, the volume is high, the data is messy, or a failure actually costs you. Custom automation gives you real error handling, retries, evals, audit logs, and AI judgment where rules fall short — wired directly to your systems through a governed layer instead of a chain of webhooks nobody can debug.
Is it safe to let automation touch my production systems?
Only with guardrails, which is most of the work. Every automated action here is rate-limited, circuit-broken, and audit-logged, with human checkpoints on anything irreversible and a green-only release gate before changes go live. The same discipline I use to let AI agents ship code to production applies to letting automation touch your business systems — controlled access, full visibility, and a way to stop it fast.