Topic guide
AI agents for business
An AI agent uses a model, context and tools to pursue a task within defined boundaries. The business decision is where that flexibility earns its cost and how its actions remain accountable.
For operations leaders, technical founders and teams evaluating tool-connected AI.
What should you decide before commissioning an agent?
Define the completed business outcome, then determine whether the work needs an agent at all. A task with stable inputs and explicit rules may be better served by a workflow. A task that needs interpretation still requires evidence, permissions and a way to stop.
Choose the operating model
Describe one real task from request to verified result. Separate the steps that follow rules from those that need interpretation. Keep the simplest architecture that can satisfy the task. A conversational interface, a model call and an autonomous action are separate choices.
Define authority
Specify the records and tools the system needs, the actions it may propose and the actions it may execute. Decide who owns exceptions. A support assistant that can read an order does not automatically need permission to refund it or export customer data.
Evaluate the outcome
Use representative tasks, including difficult and invalid requests. Measure completion, correction effort, latency and cost together. Expand authority only after observing the behaviour you need. A convincing demonstration is a starting point for evaluation, not proof of dependable operations.
Go deeper
A practical reading path.
- 01
AI agents or automation? Choose the operating model before the technology.
Start here to choose between rules, agents and a hybrid approach.
- 02
AI-agent security: define permissions before connecting tools
Turn access and action boundaries into an enforceable permission matrix.
- 03
RAG for business: building a reliable AI knowledge base
Plan an assistant grounded in maintained documents and enforceable access rules.
- 04
How to evaluate an AI feature before launch
Set task-specific acceptance criteria before releasing an AI capability.
- 05
Multi-agent systems: when does a business need more than one agent?
Assess whether specialist agents add value beyond a single system.
- 06
Designing human checkpoints into AI operations.
Design the decisions that need a person and the evidence they receive.
- 07
Observability for agentic systems: trace decisions, not just errors.
Make decisions and completed outcomes traceable in operation.
- 08
Why useful internal tools sometimes become products.
Explore when a repeatable internal capability might become a product.
Before you begin
Bring these to the discussion.
- One task owner and an explicit definition of completion.
- Known sources, permission boundaries and prohibited actions.
- A fallback when evidence is missing or a tool fails.
- Representative evaluation cases and a simpler baseline.
- A review of operating cost and human correction effort.