Most reliable AI agents are built from a small set of well-understood patterns. Choosing the simplest pattern that solves the problem is the best predictor of an agent that works in production.
1. Tool use (function calling)
The model decides when to call a defined function — search a knowledge base, look up an order, create a ticket — and uses the result in its answer. This is the foundation of every other pattern. Use it when answers depend on live data or actions.
2. ReAct: reason and act in a loop
The agent alternates between reasoning about what to do next and taking an action, observing the result each time, until the task is done. Use it when the steps can’t be known in advance — for example troubleshooting or research.
3. Plan-then-execute
The agent first writes a plan, then executes the steps, revising the plan when something fails. Plans are easier to review and audit than free-form loops. Use it when tasks have many steps and you want predictability and human review of the plan.
4. Reflection and self-checking
The agent (or a second model) critiques an output against rules or tests before finalising it — checking that a summary cites sources, a query returns plausible data or code passes tests. Use it when quality matters more than latency.
5. Routing
A lightweight classifier sends each request to the right specialist prompt, model or agent. Routing keeps each component simple and lets you use cheaper models for easy requests. Use it when requests vary widely.
6. Multi-agent orchestration
A coordinator delegates sub-tasks to specialist agents — research, analysis, drafting — and combines their results. Powerful, but every extra agent adds latency, cost and failure modes. Use it when one agent’s instructions have become too broad to perform reliably, or separate teams own separate capabilities.
7. Human-in-the-loop
Agents pause for approval before high-impact actions, or hand off to a person when confidence is low. Use it for anything financial, customer-facing, irreversible or regulated.
Choosing a pattern
| Situation | Start with |
|---|---|
| Question answering over data | Tool use / RAG |
| Open-ended investigation | ReAct with step limits |
| Long, auditable workflows | Plan-then-execute + human approval |
| High-quality written outputs | Reflection |
| Mixed request types | Routing |
| Many distinct capabilities | Multi-agent, carefully |
Production essentials for any pattern
- Trace every step; build scenario tests from real cases.
- Cap steps, time and spend per task.
- Use least-privilege tools and treat tool outputs as untrusted.
- Measure task success, not just model quality.
See how our Agentic AI services apply these patterns to real workflows.
Frequently asked questions
What is the ReAct pattern in AI agents?
ReAct (reason + act) is a loop in which an agent reasons about the next step, takes an action such as a tool call, observes the result and repeats until the task is complete.
When should I use multiple agents?
Only when a single agent's responsibilities become too broad to perform reliably or separate teams own separate capabilities. Multi-agent systems add cost, latency and failure modes.
How do I make AI agents reliable?
Use the simplest pattern that works, trace every step, test against real scenarios, limit steps and spend, use least-privilege tools and add human approval for high-impact actions.