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AI AGENT ORCHESTRATION

AI pilots are easy to start. Making them work across an organization, on your real workflows, is the hard part. I build the orchestration that does it: agents with one clear owner each, checks before anything ships, and a record of every decision.

Companies hiring for agent work ask for the same few things: someone to define what the agent does, how it is evaluated, and how it scales. Here is what I would build for each.

WHAT I WOULD BUILD FOR YOU

  1. Turn a messy workflow into an agent that does the work.

    We start from how the work is actually done today, then define what the agent does, what good looks like and who owns each step.

    I enter unfamiliar businesses and diagnose before building, with discovery and a written plan first.

  2. Split the work across specialised agents that do not collide.

    Each agent owns a defined job, with delegation rules for what it hands on and to whom.

    That is how my own company runs: specialised agents with explicit ownership boundaries and delegation rules.

  3. Evaluate agents before they ship.

    A written test of what good looks like, run end to end before release, and a gate that blocks any unsourced claim.

    End-to-end QA suites run against production, and a grounding gate blocks any unsourced claim from shipping.

  4. Review production behaviour and fix what fails.

    Agent outputs are reviewed in production, failure modes are named, and the fixes are prioritised.

    In my own system, failures and corrections are captured and turned into standing rules the agents follow.

  5. Make what works repeat across teams and customers.

    The first success becomes a playbook and a reusable capability, so the second team starts from it instead of from zero.

    Structural choices carry a written decision record, and corrections become standing rules, so the system keeps what it learns.

  6. Build the internal tools your team keeps working around.

    Internal operations agents and tools, built end to end, with product, engineering and design decided in one seat.

    I work across product and engineering in one seat: discovery, requirements and roadmap on one side; architecture, code, deployment and operations on the other.

  7. Deploy agents where the rules are strict.

    Governance, release gates and audit trails designed in from the start, for environments that cannot afford a surprise.

    Every change passes a chain of blocking gates (a secret scan, static analysis, a container scan and a dependency audit), encrypted secrets are rotated in stages, and separate release gates for function, performance and security, including LLM-safety probing, are run by QA agents that did not build the work.

  8. Put AI into your product and your customer conversations.

    Chatbots and assistants grounded in your own content, so they answer from what your business actually says.

    Website chatbots grounded in each business's own content are live on client sites, served from our chatbot platform. A retrieval (RAG) chatbot over a product catalogue of 190-plus items was stress-tested on 50 questions (44 pass, 6 partial, 0 fail); it is built.

Any of these can be the first. If your need is not on the list, bring it.

HOW IT RUNS MY OWN COMPANY

  • I designed and run a multi-agent orchestration system that operates my own company: specialised agents with explicit ownership boundaries, delegation rules and a written decision record for every structural choice, coordinating with each other and with me.
  • It learns from its own work: failures and corrections are captured and turned into standing rules the agents follow.
  • Agents are evaluated before they ship, and releases pass separate gates for function, performance and security, run by QA agents that did not build the work.
  • Work runs in parallel AI work sessions under one command-center session, which messages each of them, hands off work only after proving it was picked up, and runs fresh validation rounds until clean.
  • It runs on commercial AI models from more than one provider.
  • Production discipline: staged releases, per-tenant health monitoring with alerting, and registries that record failures honestly.
  • Security is part of the design: each agent works inside its own tool permissions, only the orchestrator can start an agent, and AI code stays in private repositories.

IN DEVELOPMENT

  • Automation of the hand-offs between those parallel work sessions is in development, and nothing on this page depends on it.
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