Capability
Agentic AI Delivery
Coding agents, MCP servers, and orchestrated subagents applied to real delivery: a working codebase, tests, and documentation at the end, not a demo.
What is actually included
- Agent-assisted build-out of full applications, themes, and integrations
- MCP servers that give agents safe, audited access to internal systems
- Subagent orchestration for parallel research, review, and implementation
- Human review gates at every point where an agent could get it wrong
What this actually looks like
A coding agent is not a code generator with better marketing. Used properly it reads the existing system first, proposes an approach, writes the change, runs the tests, and reports what it could not resolve. Running one well is mostly framing: giving it the right context, constraining what it may touch, and reviewing the diff with the scepticism you would apply to a contractor you had never worked with.
That last part is the job. The failure mode of agentic delivery is not that the code is bad – it is that it is plausible, passes review at a glance, and quietly does the wrong thing at the edges. Catching that needs someone who can read the code and knows the domain.
Where it pays off most
Well-specified build-outs
Work where the requirements are genuinely known and the constraint is purely hours: internal tools, integrations, admin interfaces, reporting layers.
Breadth-heavy work
Anything where the same change has to be made correctly in ninety places. Tedium is where the economics shift hardest.
The parts nobody wants
Tests, documentation, migration scripts, error handling. The work that gets skipped under deadline and causes the incident eighteen months later.
Where it does not
Problems where the hard part is deciding what to build. If the requirements are contested, the domain is subtle, or the real question is organisational rather than technical, an agent accelerates you toward the wrong answer faster.
A demo of an agent building a to-do app in four minutes says nothing about a codebase with fifteen years of history, an undocumented deployment, and a compliance boundary running through the middle of it. The second case is the one worth talking about.
Questions this usually raises
Does source code get sent to a model provider?
That depends entirely on the tooling and the deployment, and it is a decision to make deliberately at the start rather than discover later. There are configurations that keep everything inside a private network, and they are worth understanding before the first line is written.
How is the output known to be any good?
The same way any contributor is: tests, review, and someone accountable. Nothing about the tooling lowers the standard the code has to meet.
Can an ordinary team maintain the result?
That is a requirement, not an afterthought. The output is conventional, documented, and reviewed for readability by someone who will not always be around.