Agentic AI Development
Autonomous AI agents that plan, use tools, and complete multi-step tasks — not just single-shot chatbot replies.
Beyond a standard AI integration, agentic systems can break a goal into steps, call your internal APIs and tools, retrieve the right context via RAG, and hand off to a human when a decision genuinely needs one. We design these with the same production discipline as the rest of our work: evaluation harnesses, cost controls, and clear guardrails, not an open-ended agent making unsupervised decisions.
What's included
- Multi-step autonomous agent workflows
- Tool-calling & API integration for agents
- Retrieval-augmented generation (RAG) & semantic search
- Human-in-the-loop handoff for high-stakes decisions
- Agent evaluation, monitoring & cost controls
Technologies we use
Anthropic Claude / OpenAI APIs
LangChain / LangGraph
Python / FastAPI
PostgreSQL + pgvector
Redis
Business benefits
- Agents complete multi-step work end-to-end instead of just answering one question at a time
- Built-in evaluation and cost monitoring means the agent's behaviour and spend stay visible, not a black box
- Human-in-the-loop handoff keeps genuinely high-stakes decisions with a person, not an unsupervised model
Example use cases
An agent that triages, researches and drafts a response to inbound support or sales enquiries
Automating a multi-step internal workflow that currently requires a person to manually check several systems
A research or data-gathering agent that compiles information from multiple internal and external sources
An agent that monitors a process and only escalates to a human when it hits a genuine edge case