03 / Services / Agentic AI

Agentic AI DevelopmentAgents with clear boundaries.

Autonomous, tool-using AI agents and multi-agent systems that plan, act, and complete real business workflows end to end.

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Area: Agentic AI
Stages: 7
Related projects: 1

01 / The problem

What usuallygets in the way.

Design multi-step AI agents that plan, use tools and hand off to people - with guardrails, tracing and review where it matters.

Single prompts cannot handle multi-step work

Agents take actions nobody can trace or undo

Tool integrations are brittle and untested

02 / What I build

What you get.

A supervisor-and-specialist agent design
Tool integrations with guardrails and approvals
Traces, evaluations and a human-review path

03 / How it works

From a request to a reviewed action

A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.

Request
A goal in plain language, with user context.
Planner
A supervisor splits the goal into steps.
Tools
Specialist agents call APIs, search and databases.
Memory
State carried across steps and sessions.
Guardrails
Policies check inputs, outputs and actions.
Human review
Approval for anything costly or irreversible.
Action
The result is delivered and fully traced.

04 / In depth

The details.

I design and ship agentic AI systems - LLM-powered agents that reason, call tools, use memory, and take real actions instead of just answering questions. From single task-runners to orchestrated multi-agent teams, I build agents that reliably complete end-to-end business workflows with the right guardrails.

Autonomous Agents

Goal-driven agents that plan multi-step tasks, choose and call tools (APIs, databases, search, code execution), observe results, and self-correct until the objective is met.

Multi-Agent Orchestration

Coordinated agent teams - planner, researcher, executor, reviewer - built with LangGraph and MCP so complex work is decomposed, parallelised, and recombined predictably.

Tools, Memory & MCP

Model Context Protocol servers, function/tool calling, vector and episodic memory, and retrieval so agents stay grounded, remember context across turns, and act on your live systems.