AI assistants answer questions; AI agents go further and take actions across your systems: reading a document, updating a record, drafting a reply, raising a task. That makes them useful, and it means they need to be applied with care. Here is how to tell where they will help.
Assistant or agent?
An assistant helps a person do a task, for example drafting an email or summarising a document. An agent carries out steps of a workflow itself, using tools and data you give it, and hands over to a person where judgement is needed. Many good solutions start as assistants and grow into agents once the process is well understood.
Good candidates for agentic automation
Look for work that is frequent, follows a recognisable pattern and is mostly reading, sorting and moving information.
- Processing incoming documents such as invoices, orders or applications into structured records
- Triaging enquiries and support requests, and routing them with a suggested reply
- Drafting routine reports and summaries from data you already hold
- Keeping records consistent across systems that don't talk to each other
- Answering internal questions from your own policies and knowledge base
Poor candidates, for now
Some work should stay with people, or wait until the process around it is clearer.
- Rare, one-off decisions where there is little pattern to learn from
- High-stakes actions that can't be undone, unless a person approves each one
- Processes the team itself can't describe consistently
- Tasks where the data needed is missing, scattered or unreliable
Fix the process first
Automating a confusing process makes it confusing faster. Before building an agent, map the workflow, remove steps that add no value and agree what a good outcome looks like. Often this alone saves time, and it gives the agent a clear job to do.
Guardrails and people in the loop
Decide what the agent may do on its own, what it must ask about and what it must never do. Give it only the access it needs, log every action and make it easy for a person to review, correct or undo its work. Confidence thresholds and approval steps keep people in charge of the decisions that matter.
Prove it on a small scale
Start with one workflow and a set of real examples. Measure accuracy, time saved and how often people need to step in, then improve before widening the scope. Integrate with the systems your team already uses. An agent that lives in yet another tool is easily ignored.
Check your data and integration readiness
An agent is only as good as the information and systems it can reach. Before building, check:
- Is the data the agent needs available, reasonably clean and up to date?
- Can the systems involved be accessed safely through APIs or integrations?
- Are there clear rules or examples showing what a correct outcome looks like?
- Who in the team will own the workflow and review the agent's work?
Security, privacy and access
Treat an AI agent like a new team member with system access. Give it the minimum permissions it needs and keep credentials out of prompts. Be careful with personal or confidential data, and understand how any AI provider you use handles and stores it. Keep an audit trail of what the agent saw and did, so issues can be traced and fixed.
Measure what matters
Judge an agent by business outcomes, not by how impressive it looks in a demo. Useful measures include how accurate its work is, how much time it saves the team, how often people need to correct it and whether users trust it enough to rely on it. Review these regularly. Models, data and processes change, so performance should be watched, not assumed.
A simple first project
A practical way to start:
- Pick one frequent, well-understood workflow that frustrates the team
- Map it, simplify it and collect real examples of inputs and correct outcomes
- Build an assistant that suggests actions while a person approves each one
- Measure accuracy and time saved over a few weeks
- Automate the steps that prove reliable, keeping approval for the rest