Hire AI agent developers who ship agents that hold up past the demo

• You want to replace a scripted support chatbot with an agent that actually resolves tickets: looking up orders, issuing refunds, and updating records through your internal APIs, then handing off to a human when it is unsure.
• You have a large internal knowledge base and need a RAG assistant that answers with citations from your own docs instead of guessing, so support and sales stop digging through wikis.
• You want to automate a document-heavy back-office workflow, like pulling fields from invoices or contracts and pushing them into your systems, with checks so bad extractions do not slip through.
• You built an agent demo that works in a notebook and now need it production-ready: evals to catch regressions, tracing to debug failures, and controls on token cost and latency before real users hit it.

Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For AI agent developers, we look specifically at:

• Tool and function calling design: how they define and validate call schemas with Pydantic or zod, recover when a tool errors or returns junk, and set stop conditions so an agent does not loop forever burning tokens.
• Evals over vibes: whether they build a regression eval set with promptfoo, LangSmith, or custom harnesses to catch quality drops, since agent output is non-deterministic and unit tests alone will not catch it.
• RAG craft: their choices on chunking, embeddings, and reranking, and what the agent does when retrieval returns nothing relevant instead of letting the model make something up.
• Cost and latency control: token budgeting, routing easy steps to cheaper models, caching, and reading traces in tools like Langfuse or LangSmith to find the step that is slow or expensive.
• Safety scoping: limiting which tools an agent can call, guarding against prompt injection hidden in retrieved content, and keeping a human in the loop for actions that are hard to undo.