AI Agents & Agentic Workflows

We build task-specific single agents, multi-agent systems where specialised agents coordinate on complex workflows, and long-running autonomous agents with persistent memory and state. Common use cases include customer query handling, document processing, research and synthesis, operational monitoring, and multi-step decision workflows across connected systems.

Every agent we build includes defined tool boundaries, guardrails against hallucination and off-scope actions, human-in-the-loop escalation paths for edge cases, structured audit logging, and a monitoring layer that surfaces failure modes before users encounter them. We do not deploy agentic systems without a tested fallback path.

We work with LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK, selected based on the orchestration complexity and production requirements of each use case. For most enterprise workflows requiring deterministic control flow and audit trails, LangGraph is our default. For collaborative multi-agent patterns, CrewAI and AutoGen are preferred depending on team structure needs.

RPA and standard automation follow fixed rules on structured inputs. An AI agent reasons over unstructured inputs, selects from a set of tools, handles ambiguity, and adapts its approach based on intermediate results. Agents are suited for tasks where the input varies, the path is not always predictable, or judgment is required before taking the next step.

Our AI agent development services follow a fixed delivery structure: a use case scoping session to define the agent's goal, tool set, and escalation criteria, then a prototype delivered within 48 hours for validation on real data. After prototype sign-off, we build the full orchestration layer, connect production integrations, implement guardrails and logging, and run load and edge-case testing before deployment. Most engagements from scoping to production take 6–10 weeks.