An agentic AI system plans and executes multi-step tasks autonomously, using tools and making decisions, rather than responding to a single prompt. Best suited to workflows with clear success criteria, built with defined guardrails and approval checkpoints for consequential actions.
What's included?
- Workflow scoping to confirm the task has clear, measurable success criteria
- Agent design: planning logic, tool access and decision boundaries
- Guardrails and human approval checkpoints for consequential actions
- Monitoring and iteration once live
Agents are useful, and oversold
AI agents that can plan and complete multi-step tasks are real, and some are genuinely valuable. But the market is full of hype. Gartner predicted in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear value or weak risk controls, and estimated that only around 130 of the thousands of vendors claiming agentic AI actually offer it (Gartner, June 2025). We start with the business case and the risks, not the technology.
Where agents tend to work
- Research and summarisation across many sources, with a human reviewing the output
- Operations workflows with clear rules: triage, routing, data entry, report preparation
- Marketing operations: campaign QA, reporting, content briefs and competitor monitoring
- Customer service for well-defined questions, with an easy route to a person
A lesson worth remembering
Klarna's AI assistant handled 2.3 million conversations in its first month, two thirds of its customer service chats, with resolution times falling from 11 minutes to under two (OpenAI, Klarna case study). A year later, its CEO said the company would invest in human support again, because customers must always be able to reach a person (CX Dive, May 2025). Agents work best when they handle the routine and hand the rest to people.
How we build
- Scope: one workflow, one measurable outcome, one owner
- Guardrails: permissions limited to what the agent needs, approval steps for anything irreversible
- Evaluation: test sets and success criteria before launch
- Audit: logs of every action and decision, reviewable by your team
- Handover: documentation and training so your team can run and improve it
If a task cannot be described clearly enough for a new employee to follow, it is not ready for an agent. We often spend the first week tightening the process itself.
How we measure it
Time saved, error rates against a human baseline, cost per completed task and how often the agent escalates. See custom LLM skills for simpler, single-task automation.