Agentic AI Implementation Guide
A guide for technology leaders who want to move agentic AI from demos into production — how to pick the right use cases, design an architecture that stays controllable, and govern agents that take actions on your behalf.
What you'll take away.
- How to tell a good agentic use case from a poor one
- Reference architecture for agents, tools and memory
- When to use an agent and when deterministic automation is better
- Governance controls for agents that take actions
- A path from pilot to production
The guide in brief.
What makes AI agentic
An agent plans steps, uses tools and acts towards a goal rather than answering a single prompt. That makes it powerful for multi-step work, and it also means its actions need the same controls you would apply to a new member of staff.
Choosing the right use cases
Good candidates involve several steps across systems, tolerate some variation and have a clear definition of done. Poor candidates are high-risk, irreversible or already well served by simple rules.
Agents or deterministic automation
Not every workflow needs a model making decisions at run time. Where steps are predictable and must be auditable, compiling the workflow to deterministic code is often safer and cheaper; reserve agents for genuinely open-ended work.
Reference architecture
A production agent needs a model, a set of well-scoped tools, access to the right context and somewhere to record what it did. Keep tools narrow, give each agent least-privilege access, and log every action.
Human oversight
Decide in advance which actions an agent can take alone and which need approval. Make approval requests clear and quick to act on, or people will either rubber-stamp them or route around them.
Evaluation and monitoring
Test agents against realistic scenarios before release and keep watching them afterwards. Track failures, escalations and cost so you can see whether the agent is helping and when it is drifting.
Where we can help.
Agentic AI
Multi-agent workflows that plan, act across your systems and pause for human approval where it matters.
AI Automation
Business process automation and intelligent workflows that stay predictable, auditable and cost-controlled.