Use Cases
AI Agents
Self-driving software that does the work, not just the planning.
I build agents that pick up tickets, edit code, run pipelines, write daily content, and ship deployable output — not chatbots. Pricing is per hour of agent runtime, not per seat.
- Always-on managed agents starting at £1/hour
- Built on Claude Code, Claude Cowork, and custom MCP servers
- Auditable runs: every action logged, every output reviewable
Why
Chatbots deflect. Agents close. The unit economics flip the moment an agent finishes a ticket, ships a PR, or publishes a piece of content without a human touching the keyboard.
How
- Map the work to a deterministic pipeline first
- Drop in an agent at the steps that benefit, not the whole stack
- Log every run; humans review the diffs, not the keystrokes
Proof
- Always-on agents running
- 13
- Hourly rate
- from £1
- Audit log coverage
- 100%
AI Agents — perceive, decide, act, observe
The agent loop
Hover or tap a node to see details.
FAQ
- What separates a real agent from a chatbot with extra steps?
- A real agent perceives state, decides on the next action from a tool set, executes, and observes the outcome — autonomously, with audit. A chatbot just round-trips text. The loop is the agent.
- How do you keep agents from going rogue?
- Allow-listed tools, hard cost ceilings, audit logs, human-in-the-loop for high-risk actions, and replayable runs. Constraints in code; trust grows with runtime evidence.
- Can agents collaborate?
- Yes — Claude Cowork pattern: planner ↔ builder ↔ reviewer ↔ deployer, with shared task state. Strict role separation beats one super-agent every time.
In production
- ABC Squad — 19 agents in production
Discovery → Strategy → Design → Engineering → Distribution chain — every client engagement runs through it.
See it - Daily content engine — 6 streams
PAD · LAD · IAD · AAD · VAD · BAD running daily on AIOS — one operator approving.
See it - Audit-traced runs
Every agent action logged with input + decision + output. Compliance-grade trail by default.