AI Agent Engineering & Troubleshooting
I design, implement and troubleshoot AI agents that connect models with tools, data and backend systems. The work can cover MCP integrations, persistent memory, retrieval-augmented generation and the infrastructure required to run an agent reliably.
When this service fits
A useful starting point is an existing stack, a reproducible failure or bottleneck, and a result that can be checked. Typical situations include an agent losing context between sessions, retrieving irrelevant material, calling tools inconsistently or depending on an integration that is difficult to observe and test.
How the work starts
We define a bounded first stage before implementation. That stage identifies the current behavior, trust boundaries, available evidence and acceptance criteria. For troubleshooting, the first deliverable may be a documented diagnosis. For implementation, it may be a working vertical slice that proves the integration path before the scope expands.
What the delivery includes
The delivery includes the agreed implementation or diagnosis, verification against acceptance criteria, and a handover that explains configuration, operational assumptions and remaining limits. Interfaces and failure modes are made explicit so that a team can reproduce the result rather than depend on a demonstration that only works once.
Limits and measurement
The stack, hosting and model are selected from the requirements rather than fixed in advance. No performance, accuracy or cost improvement is promised without a baseline and a repeatable measurement. Agent autonomy is increased only when permissions, error handling and verification support it.