AI workflows built to scale
Multi-agent pipelines with human oversight, audit trails, and deterministic quality checks. Not a chatbot — a production system.
Your automation has no guardrails
Chatbot-grade automation
You chained a few API calls, added a prompt, and called it automation. It works — until it doesn't. No quality gates, no fallback logic, no way to know why it produced the wrong output. Prompt chains are prototypes, not production systems.
No audit trail
When an AI workflow makes a decision, who approved it? What data did it use? What did it cost? Without structured provenance and observable pipelines, your automation is a black box — and a liability under the EU AI Act's high-risk system rules taking effect August 2026.
Scaling from one agent to many
One agent is manageable. Seven agents across two departments, each with different models, budgets, and quality requirements? That needs a control plane — not more prompt engineering. The same challenge applies to scaling content pipelines or financial workflows.
From prompt chain to governed system
Process mapping
I identify the manual workflow with the highest automation ROI — not the easiest one to automate, but the one where automation creates the most business value. We map inputs, decision points, quality gates, and handoffs.
Agent architecture
I design the agent roster, tool set, and control plane. Each agent gets a defined role, model, budget, and governance rules. The system knows who does what, who approves what, and what happens when something fails.
Build & orchestrate
Production development with deterministic quality checks at every pipeline stage. Agents coordinate through a control plane with heartbeats and task queues — not brittle sequential chains. You see working pipelines weekly.
Deploy & monitor
Ship to production with full observability — cost tracking, token usage, latency, error rates. The system self-reports its health. Pipelines evolve as your needs change; new agents slot into the existing governance framework.
7 agents, 2 departments, zero chaos
Data operations and editorial — two departments with distinct agents, models, and budgets, all coordinated through a single control plane. A governed organisation of AI workers — each with defined roles, budgets, and approval chains.
Agents don't write raw queries. They call typed, validated tools for each entity — articles, brands, pipelines, translations, audits. Every action is traceable, every input validated with schemas.
Full editorial workflow — research, writing, review, voice check, translation — for under three dollars. Budget tracking per agent means you know exactly what each step costs. Backed by 1,500+ automated tests across the pipeline. Read how it was built.
From data to publication
- 7-step editorial pipeline: research, write, caption, review, voice consistency, voice review, and translation
- Agent control plane with heartbeat monitoring, per-agent budgets, and deterministic task queue coordination
- 21 typed MCP tools giving agents structured database access — no raw queries, full provenance on every action
Built for governance
Common questions
How is this different from just chaining API calls?
API chains are sequential, fragile, and opaque. A governed multi-agent system has a control plane that coordinates agents via heartbeats and task queues, enforces per-agent budgets, and requires approval for high-stakes actions. When an agent fails, the system retries, escalates, or halts — it doesn't silently produce garbage. It's the difference between a script and a production platform. For software with no API at all, computer use automation takes a different approach — agents that operate the interface directly.
What happens when an AI agent produces wrong output?
Every pipeline has deterministic quality gates. In the editorial system, an Editorial Judge agent reviews every article against editorial standards before publication. A Voice agent enforces brand consistency. A Translator preserves terminology. Each gate can approve, reject with feedback, or escalate to a human. Nothing ships without passing every gate.
How do you test AI workflows?
The same way you test production software — with 1,500+ automated tests. Unit tests for individual tools and calculation logic, integration tests for pipeline stages, end-to-end tests for complete workflows. Langfuse tracing gives per-request observability: cost, latency, token usage, and output quality. The research pipeline is tested as rigorously as the editorial one.
What happens when AI models get updated or deprecated?
The governance framework abstracts model assignments per agent. When a better model ships, you update the assignment — the pipeline, tools, and quality gates stay unchanged. In production, the editorial system already runs multiple models side by side: Sonnet for volume tasks, Opus for editorial judgment. Adding a new provider or swapping a model is a configuration change, not a rewrite.
What about EU AI Act compliance?
The patterns you need for compliance — human oversight dashboards (Art. 14), audit trails (Art. 12), risk management documentation (Art. 9) — are the same patterns that make AI workflows reliable. Every agent action is logged with full provenance. Budget controls prevent runaway costs. Human approval gates are built into the governance framework. Compliance is a byproduct of building it right.
Your manual process has a cost.
Let's find the workflow where automation creates the biggest return.