Agentic AI & Automation Services
Agentic systems engineering for organisations that need more than a demo.
I help teams discover, design, build, integrate, and review agentic automations that connect safely to real workflows and enterprise systems. The emphasis isn't on making software appear autonomous—it's on producing useful outcomes within explicit permissions, budgets, evidence requirements, and human decision boundaries. Background and credentials live on About.
What I help you build
Agentic workflow discovery
Teams deciding where agentic automation earns its complexity.
Workflow modelling, opportunity assessment, agent-vs-deterministic decisions, risk analysis, and a target architecture with a phased plan.
Read: Why Build an Agent Harness →Agentic automation & integrations
Teams that need agents doing real multi-step work, not a chat demo.
Bounded, tool-using agents wired into your APIs, data, documents, and queues—built on explicit contracts, least-privilege access, and safe failure.
Read: Inside the Sandbox →Agent platform & harness architecture
Teams whose agent logic has outgrown a single prompt and a loop.
The control plane around the model: identity, tool contracts, sandboxing, budgets, stop conditions, memory, human approval, and audit trails.
Read: Why Agent Swarms Need Worker Leases →Evaluation, security & governance
Teams whose dashboards look healthy while jobs still fail.
Task-level success criteria, deterministic checks, adversarial and prompt-injection testing, regression suites, and escalation policy.
Read: Catching Hallucinated Paths →Production hardening
Systems already moving beyond prototype that need to survive operations.
Reliability, tracing, latency and cost, context management, retry and repair policy, concurrency, and incident analysis.
Read: Bounded Agent Repair Loops →Architecture & runtime review
Teams with an agent, RAG system, or GenAI platform who need an outside read before wider rollout.
A written audit of design gaps, failure modes, and operational risk—with ranked, actionable recommendations.
Author background →Ways to engage
- Architecture or runtime review — A bounded independent review of an existing agent, automation, RAG platform, or GenAI architecture, with a written assessment of risks, gaps, and prioritised recommendations.
- Discovery and design engagement — A focused examination of a workflow or automation opportunity before significant implementation investment.
- Pilot implementation — A bounded build that validates the workflow, integrations, operating model, and success criteria—not merely a conversational interface.
- Production delivery — Implementation or technical leadership for a defined agentic automation, integration, or platform capability, including testing, controls, and operational handover.
- Ongoing technical advisory — Periodic architecture, evaluation, and delivery support for teams building agentic systems internally.
How engagements work
- Describe the workflow — The work being done today, the systems involved, the recurring friction, and the outcome that matters.
- Define the boundary — What the system may access, which decisions it may make, where human approval is required, and what failure must look like.
- Choose the smallest credible intervention — An architecture review, a deterministic automation, an agent-assisted workflow, a bounded pilot, or a production build.
- Validate the outcome — Completed work, failure behaviour, security boundaries, cost, and operational evidence—not only response quality.
- Prepare for operation — Ownership, controls, monitoring, exceptions, escalation paths, and the conditions under which the automation should stop.
Engineering principles
- Bounded authority rather than assumed autonomy
- Deterministic checks around probabilistic behaviour
- Least-privilege integrations
- Human control over consequential actions
- Evidence before confidence
- Explicit budgets and stop conditions
- Reproducible evaluation
- Honest treatment of failure and uncertainty
What this is not
- A generic chatbot installation service
- Vendor-led model selection disguised as architecture
- Unrestricted automation with broad production credentials
- A promise that every workflow needs an agent
- Success claims based only on demos, token metrics, or subjective response quality
Start a conversation
Prefer a short written brief over a cold pitch call. A useful initial brief includes:
- The workflow or system you want to improve
- The people and systems involved
- The current bottleneck or failure mode
- The action you want the automation to perform
- Decisions that must remain with a person
- Security, regulatory, cost, or delivery constraints
- How you would recognise a successful outcome
The initial objective is to determine the smallest safe and useful intervention—not to force an agent into the problem.
Inquiries: [email protected]