An AI agent is a language model that does not just answer but acts: it decides which tools to call, reads results, plans further steps and executes them. In a SaaS product that can mean an agent creates records, drafts emails, moves appointments or compiles reports from several sources. That is powerful — and that is exactly why the most important design question is not what an agent can do, but what it is allowed to do.
Where agents add value
Agents are strong at tasks made of several fairly variable steps whose result a person can easily check. Research across several internal sources, preparing a quote from customer data and templates, sorting and pre-answering incoming requests, preparing data for a report: in all these cases the agent saves work and an error is visible before the result is used.
Agents are weak at tasks that always run the same way. There, a classic, hard-coded workflow is faster, cheaper and more reliable. An agent that reconsiders every time how to book an invoice is worse than a rule that always gets it right.
Autonomy in levels
It helps to grade autonomy deliberately. At level one the agent suggests and a person executes. At level two the agent executes, but only after explicit approval. At level three it acts on its own within narrow limits, and people review afterwards. Most features in business applications belong at level one or two. Acting independently pays off only for actions that are easy to reverse and have limited consequences.
Tools with narrow permissions
An agent is only as safe as the tools it can call. Each tool should do exactly one thing, operate with the permissions of the user on whose behalf the agent acts, and validate its own inputs. A tool «run database query» is too powerful; a tool «fetch this customer's open orders» is appropriate. Write tools should be idempotent so a repeated call after an error does not book or send twice.
Take prompt injection seriously
Agents read content that does not come from the user: emails, documents, web pages, tickets. That content can contain instructions the model mistakes for genuine tasks — such as a request to send data to an outside address. No clever wording in the system prompt fully protects against this prompt injection. What works are structural limits: tools that simply cannot send data outside, approvals for consequential actions, and a clear separation between what the user asks for and what the agent merely reads.
Logs and traceability
Every step of an agent should be logged: the task, the reasoning, which tools with which inputs, which results. That is indispensable for debugging, a trust argument for customers, and depending on the application also a regulatory requirement. Users should be able to see in the product what the agent did and undo actions where possible.
Limiting cost and runtime
Agents often make many model calls in sequence, and a misunderstood task can lead into a long loop. An agent therefore needs hard limits: a maximum number of steps, a maximum runtime, a budget per task. When a limit is reached, the agent stops and reports how far it got instead of trying again.
Regulatory frame
Under the EU AI Act, people must be able to recognise that they are interacting with an AI system when that is not obvious. If an agent makes decisions in sensitive areas — job applications, credit or access to essential services — the application may count as high-risk AI, with obligations from 2 December 2027. Under data protection law the usual rules for AI processing apply: purpose limitation, data minimisation and the limits on solely automated decisions.
A good starting point
The best first agent in a SaaS product is one that prepares rather than decides: it gathers information, drafts an action and presents it to the user for approval. That way the team learns how the agent behaves, users build trust, and the tools mature before more autonomy is granted. Innopulse builds such features with narrow tools, approvals and complete logging.