AI Agent Engineering for Funded Startups
Autonomous agents that survive production — with tool use, memory, evals, and observability.
AI agent engineering is the work of turning a prompt that works in a playground into a system that holds up in production — with tool use, memory, evaluations, observability, and cost controls. I build and lead that work for funded SaaS founders, using the Claude Agent SDK, LangChain, and custom orchestration on Laravel and Node stacks. The difference between a demo and a product is almost never the model; it is the evals, guardrails, and instrumentation around it.
When this helps
- A prototype agent works in testing but is unpredictable with real users.
- Nobody can say whether a prompt change made the system better or worse.
- Token spend is unpredictable and nobody owns the cost line.
- There is no visibility into why the agent did what it did.
What the work includes
Agent architecture
Deciding what should be an agent at all, then designing tools, memory, and control flow around a clear output contract rather than an open-ended prompt.
Evaluation harness
A realistic eval set so every prompt or model change is a measurable delta instead of a guess. This is the single highest-leverage thing most teams are missing.
Observability
Logging prompts, responses, tool calls, retries, token counts, and cost, so failures can be explained rather than reproduced by luck.
Guardrails and cost caps
Rate limits, spend caps, content filtering, and prompt-injection defences in place before real traffic arrives.
Gradual rollout
Shipping to a small slice of traffic and widening as the eval set holds, rather than a single all-at-once launch.
Team enablement
Leaving your engineers able to run and extend the system, not dependent on the person who built it.
Related work
Questions
What makes an AI agent production-ready?
Structured output the application can validate, an eval set that makes changes measurable, observability over prompts and tool calls, and guardrails on cost and content. A prototype that lacks these is a demo, regardless of how well it performs in a manual test.
Which models and frameworks do you work with?
Claude and GPT-4 as the primary models, with the Claude Agent SDK, LangChain, and custom orchestration depending on what the system actually needs. Framework choice follows the problem rather than the other way round.
Do you build from scratch or fix an existing agent?
Both. A common engagement is taking an agent that works in a demo and adding the evals, observability, and guardrails it needs before it can carry real traffic.
How do you keep AI costs under control?
Cost is treated as a design constraint: right-sizing the model per task, caching where it is safe, capping spend, and logging token usage per call so the number is visible before it becomes a surprise.
Can you work with our existing engineering team?
Yes. Most of my work is leading and working alongside client teams. I have managed cross-functional teams of 5 to 45 engineers over the past 7+ years.
Want to talk it through?
Tell me what you are building and where it is stuck. I work remotely with founders across the UK, EU, US, and MENA, in hours that fit your timezone.
Get in touch →