AI Agent Patterns for SaaS Products

The AI conversation has moved past chatbots. The question now is: can AI take actions, not just answer questions?

An AI agent doesn't just tell you how to export a report. It exports the report. It doesn't just explain the API. It writes the integration code and tests it. It doesn't just answer support questions. It resolves the tickets.

For SaaS products, agents represent a fundamental shift in what's possible. But they also represent significant complexity, risk, and design challenges that most teams underestimate.

What Makes an Agent Different from a Chatbot

A chatbot receives user text, generates response text, and returns it. The chatbot's only output is text to the user. An agent receives user text and can respond with actions: API calls, database mutations, external service invocations, or multi-step workflows.

Agents require tool definitions, tool invocation parsing, an execution layer, state management, error handling, and authorization. The complexity jump from chatbot to agent is substantial.

Agent Patterns for SaaS

Pattern 1: The Task Executor

The simplest useful agent pattern. The user provides a clear task; the agent executes it. Single-turn, limited tools (3-10), direct mapping from intent to action, with confirmation before irreversible actions.

Pattern 2: The Workflow Automator

Multi-step workflows where the agent orchestrates a sequence of actions. Requires state management, handles sequential dependencies, and needs explicit logic for partial success handling.

Pattern 3: The Conversational Agent

The agent maintains a longer conversation, taking actions interleaved with clarifying questions and confirmations. Harder to bound - requires guardrails like action limits per turn and explicit confirmation for irreversible actions.

Pattern 4: The Autonomous Background Agent

Operates without direct user interaction, triggered by events or schedules. Requires stricter scope limits, heavier logging, and safeguards like rate limits, dry run mode, and kill switches.

Tool Design for Agents

How you design tools affects agent reliability more than how you design prompts. Keep tools narrow, include constraints in descriptions, design for composition, and handle errors gracefully with structured error responses.

Authorization and Safety

Agents inherit permissions from the user but need explicit enforcement. Apply the principle of least privilege, set scope limits, require confirmation for high-risk actions, and maintain comprehensive audit logging.

Key Takeaways

• Start with task executors. Single-turn, focused agents are simpler and safer.
• Design tools for narrow, composable actions. Tool design matters more than prompt design.
• Enforce authorization at the tool layer.
• Build for graceful failure with rollback and recovery logic.
• Log everything. Agent behavior is harder to predict; observability is essential.