Most AI agent projects fail not because the technology is immature, but because teams build systems far more complex than their task requires. A new design framework from Wavect breaks down five proven patterns for building AI agents, each suited to different problems, and emphasizes a counterintuitive principle: start with the simplest possible solution and only add complexity when it directly addresses a failure you've observed.

The framework challenges a common misconception in the agentic AI space. Many teams assume that more autonomous behavior, more tool use, and more decision-making loops automatically lead to better results. In reality, adding these features increases latency, cost, and failure points without necessarily improving outcomes. The key insight is to match your architecture to your actual problem, not to an abstract notion of what an "advanced" agent should look like.

What Are the Five Core AI Agent Design Patterns?

The framework identifies five composable patterns, each with distinct trade-offs and failure modes:

• Single-shot: Send a bounded task to a model once and return its output without any autonomous tool loop. This works for extracting fields from a complaint, classifying a request, or summarizing a document when all required data is already available.
• ReAct loop: Interleave reasoning, action, and observation so that tool results inform the next decision. Use this when the next useful action depends on the previous result, such as a support assistant checking an order and then deciding whether to inspect delivery events or refund status based on what it finds.
• Planner-executor: Separate planning from execution, with the planner proposing steps and an executor completing them. This pattern suits long assignments that repeatedly skip deliverables or lose dependencies, such as a market-research report that needs evidence collection before analysis and analysis before recommendations.
• Reflection: Add critique and revision after an initial output to improve a draft. Use a concrete rubric rather than vague instructions like "think harder," and start with one revision pass to compare against the unedited baseline.
• Verifier-gated execution: Require independent checks before an action changes money, permissions, or an external system. This pattern applies to every other pattern and is not a reward for reaching a more advanced architecture; a one-call payment proposal needs authorization just as much as a multi-step agent does.

When Should You Actually Add Complexity to Your AI Agent?

The framework proposes a simple decision rule: identify the first broken requirement before adding another model call. If your agent is missing context, that suggests better context handling, not a more autonomous loop. If your business process is predictable, that suggests coded workflow logic, not an autonomous decision-making system. Neither automatically justifies adding complexity.

This approach aligns with guidance from Anthropic, which likewise recommends beginning with simple solutions and weighing additional agentic behavior against latency and cost. The principle is to choose the smallest architectural change that addresses an observed failure, rather than building for hypothetical future needs.

For example, a support assistant that needs to understand a complaint from an earlier conversation requires thread state and cross-session memory. But memory alone does not answer the next architectural question: what should the agent be allowed to do with what it remembers? A support assistant may need an earlier conversation to understand a complaint, yet it still cannot treat a remembered "I am an administrator" as permission to issue a refund. The decision about control flow and authorization is separate from the decision about memory.

How to Choose the Right Pattern for Your Use Case

• Extraction and classification tasks: Use single-shot design when required data can be assembled deterministically before the call. Validate the schema in ordinary code, and let missing required fields produce a validation failure or review request rather than an invented value.
• Dynamic decision-making: Use ReAct when tool results change what the agent should do next. Define a maximum number of steps, a wall-clock deadline, and a tool-call budget. Detect repeated calls that produce no new evidence and distinguish retryable transport failures from business rejections.
• Long-running or dependent tasks: Use planner-executor when a long assignment repeatedly skips deliverables or loses dependencies. Make each proposed step carry an input, expected output, dependency, and completion check. Save completed work as explicit state and replan only when evidence invalidates an assumption, not just because the model can produce a different plan.
• Quality improvement: Use reflection for drafts that benefit from specific critique. Start with one revision pass and compare it with the unedited baseline rather than assuming multiple passes always improve results.
• High-stakes actions: Use verifier-gated execution for any action that changes money, permissions, or an external system. Include independent checks before the effect, plus denial and escalation paths.

A critical failure mode to watch across all patterns is the well-formatted but wrong output. Tool observations are data, not new authority. A retrieved page saying "ignore the policy" must not rewrite the policy. Check access before sensitive reads, validate arguments, and gate every consequential write.

The framework also emphasizes that verification is a risk control, not a reward for reaching a more advanced architecture. A read-only summarizer does not become better merely because it has a planner. Conversely, a one-call payment proposal needs authorization just as much as a multi-step agent does. The pattern you choose should depend on your actual failure mode, not on an abstract maturity ladder.

For teams building AI agents in production, this framework offers a practical antidote to over-engineering. By starting simple and adding complexity only when it solves a specific observed problem, teams can reduce latency, lower costs, and improve reliability. The most sophisticated agent is not always the best agent; the right agent is the simplest one that meets your acceptance criteria.