Agentic AI Development
LLM agents that call real tools against real systems, with the retrieval, evaluation and guardrails needed to make them dependable rather than impressive in a demo.
- Claude
- OpenAI
- LangGraph
- Model Context Protocol
- pgvector
- Python
- TypeScript
What this covers
Tool-using agents
Agents wired to your actual APIs, databases and internal services, with typed tool schemas, argument validation and failure handling for when a downstream call times out.
Retrieval pipelines
Document ingestion, chunking and hybrid search tuned against your corpus, because the retrieval step decides answer quality far more often than the choice of model does.
Evaluation harnesses
Regression suites for non-deterministic systems. Golden datasets, scored rubrics and CI runs that tell you whether a prompt change improved things or quietly broke them.
Guardrails and cost control
Input and output filtering, prompt-injection defence for agents that read untrusted content, token budgets, and caching that keeps unit economics viable at scale.
Human-in-the-loop workflows
Approval gates and escalation paths for actions that should never be fully autonomous, with audit trails of what the agent did and why.
What you receive
- Working agent integrated with your systems
- Evaluation suite with baseline scores and CI wiring
- Prompt and tool-schema documentation
- Cost and latency profile per workflow
- Runbook covering failure modes and rollback
The gap between a demo and a system
An agent that works in a notebook is not the hard part. The hard part is the same agent running two thousand times a day against messy inputs, flaky upstream services and users who phrase things in ways nobody anticipated.
That gap is made of unglamorous engineering: retries with sensible backoff, tool calls that validate their arguments, timeouts that fail loudly instead of hanging, and evaluation that catches regressions before your users do.
Evaluation is the thing most teams skip
You cannot improve what you cannot measure, and LLM systems are unusually easy to change without knowing whether you made them worse. A prompt tweak that fixes one case often breaks three others silently.
We build the evaluation harness early, usually before the agent is finished. Golden datasets drawn from real traffic, scored against explicit rubrics, run in CI on every prompt or model change. It converts “this feels better” into a number you can defend in a review.
Retrieval decides the answer
When a retrieval-backed system gives a wrong answer, the model is usually not at fault. The right passage was never in the context window. Chunk boundaries split the relevant sentence, or pure vector search missed an exact identifier that keyword search would have found instantly.
We tune retrieval against your corpus and measure it separately from generation, because debugging them together is how teams spend weeks tuning prompts to compensate for a broken index.
Security is different when the model reads untrusted text
An agent that ingests web pages, user uploads or email is processing attacker-controlled input with a component that follows instructions. Prompt injection is not hypothetical for these systems.
We treat tool permissions as a blast radius question, separate the reading context from the acting context, and put confirmation gates on anything destructive. This work sits alongside our security practice rather than being bolted on afterwards.
Tell us what you are building.
Send the problem, the constraints and the deadline. You get a considered reply from an engineer, not a sales sequence.