AI Solutions · AI Agents & Solutions
AI Agents & Solutions
Almost every organisation’s first agent is a demo. A demo proves an agent can succeed once. Everything between that and something you would put in front of a customer is the actual work. [Draft copy]
The problem
Your engineers are already doing this. The question is under what discipline.
AI is in your delivery whether or not it is in your policy, and the failure mode has changed with it. The errors are no longer syntax mistakes that fail loudly. They are wrong assumptions about business logic, missing edge cases, and architectural choices that cost you in eighteen months. They are hard to catch precisely because the code looks right and passes the obvious tests.
The industry has a name for the loose end of this: telling a board that the team is vibe coding the payments integration should raise alarm, and it does. The problem is that most organisations cannot say which end of that spectrum they are actually on, because nobody has defined the difference.
What enterprise-grade looks like here
The differentiator is not whether you use AI. It is how the output gets verified.
There are two mechanisms and you need both. Tests verify the deterministic part: given this input, the system produces that output. Evaluations verify the part that is not deterministic: did the agent take a sensible path, choose the right tools, and produce something that meets the bar. Without both, it is vibe coding however sophisticated the prompts are.
The second thing enterprise-grade means here is that an agent is a model plus a harness. The model is one input. The rule files, tool definitions, sandboxes, orchestration, hooks and observability around it are the rest, and they are your surface area rather than the model provider’s. When an agent misbehaves the instinct is to blame the model; far more often it is a missing tool, a vague rule, an absent guardrail, or a context window stuffed with noise. Most agent failures, examined honestly, are configuration failures.
And the last stretch is the expensive one. An agent will get most of a feature quickly; the remainder — edge cases, error handling, integration points, the subtle correctness requirements — is exactly where your regulatory and contractual obligations live. That is the part we scope for rather than discover.
This framing repeats on every capability page. It is the differentiator, and it reads consistently across all of them (§9 T3).
What we do
Five moves, in this order.
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Place each workload on the spectrum, deliberately
Exploration can be fast and loose. A system touching money, personal data or a regulated process cannot. We write down which is which, per project and per environment, because teams that leave this blurry ship prototypes by accident.
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Engineer the harness, not the prompt
Rule files and instructions, the tools the agent may call, the sandbox it runs in, the orchestration between specialists, and deterministic hooks for the things it must never do. This is where the reliability actually comes from.
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Engineer the context
What the agent always carries versus what it retrieves on demand is a real architectural decision with a real cost, and we treat it as one — reviewed and versioned like any other configuration, not accumulated by whoever edited it last.
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Set the bar at the eval, not the demo
Output evals check what was produced. Trajectory evals check how it got there, because a fluent answer that skipped its verification steps is more dangerous than a visible error. Both run in CI against an explicit rubric.
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Hand over the practice, not just the agent
Your engineers learn both modes — directing an agent in real time, and delegating well-specified work to run unattended — and how to review generated code for the failure modes it actually has.
[Draft copy]
What you end up owning
Documents and configuration, not a dependency.
| Deliverable | Owned by |
|---|---|
| Agent definitions and harness configuration, in version control | Engineering |
| Rule and instruction files, reviewed like code | Engineering |
| Test and eval suites with explicit rubrics, running in CI | Engineering |
| Traces of every agent run | Operations |
| A written policy on which work runs at which discipline | Engineering leadership |
[Draft copy]
Tell us what you are being asked to prove.
Bring the agent that works in a demo and worries you in production.
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