Agentic AI & Workflow Automation

Agentic AI combines models, tools, memory, policies and orchestration so a system can perform multi-step work while remaining observable, testable and interruptible.

Matchpoint approaches AI as an operating capability with accountable owners, explicit decision gates, measurable acceptance criteria, documented architecture and a practical path from discovery to production.

Agentic systems introduce planning, tool use, memory and multi-step state into a workflow. We begin by decomposing the job into bounded responsibilities and identifying which actions can be automated, which require evidence, which need approval and which should remain with a human decision-maker.

The system design covers agent boundaries, tool contracts, permissions, state, memory, policies, evidence, handoffs and exception paths. Deterministic controls manage stable rules and high-consequence checks; models handle interpretation and planning within those boundaries. Every action produces a trace that can be evaluated and investigated.

Testing covers task completion, tool selection, evidence use, state consistency, recovery from partial failure, escalation behaviour, latency and unit cost across realistic multi-step scenarios. Production monitoring focuses on workflow outcomes and action quality as well as individual model responses.