Designing AI Agents That Don’t Misbehave

AI agents look powerful on paper. They can plan, take actions, call tools, and adapt to changing inputs. This makes them tempting for teams building internal assistants, workflow automation, or mobile apps with complex logic – especially for companies comparing mobile app development agencies or trying to hire AI developers who understand real-world risks. But once these agents leave the sandbox, things shift. They wander off-task. They repeat actions. They skip steps. They “create” facts. They follow instructions too literally or not literally enough.

The cause is simple. LLMs were not built to behave like deterministic workers. They were built to generate text. Turning them into autonomous actors requires more than prompting. It requires engineering patterns that keep their behavior predictable.

The root problem: free-floating agents have no structure

When you give an LLM the freedom to decide its next step, you also give it the freedom to make mistakes. The model improvises. It tries actions that don’t make sense. It loops. It takes shortcuts that violate the workflow. The system may look “smart” in a demo, but it collapses under real load.

Key reasons:

• LLMs can’t reliably track long sequences without help.
• They generalize poorly when state becomes complex.
• They guess when uncertain rather than stop.
• They treat every next step as text, not logic.

So the question is no longer “How do we make the agent smarter?” but “How do we make the agent safer?”

State machines: the first line of control

A state machine turns a chaotic agent into a predictable one. Instead of letting the model choose anything at any time, the system restricts it to a finite set of states and transitions.

Example states:

1. Collect info
2. Verify inputs
3. Take action
4. Review output
5. Complete or escalate

The model can still generate suggestions, but the system enforces the next legal move. This reduces drift, looping, and overreach. It also makes logs easier to audit, since every step follows a defined path.

A well-designed state machine gives the model freedom inside boundaries. It cannot jump ahead or skip steps. And when the state machine blocks a move, you understand why.

We’re grateful to the S-PRO development team for sharing their experience for this article.

Task graphs: planning without chaos

Some workflows can’t be captured in a single linear path. They branch. They require waiting. They depend on multiple tools. This is where task graphs help. A task graph defines:

• The tasks
• The dependencies between them
• The allowed order
• The required validation at each stage

Instead of asking the model to “figure it out,” the system walks the graph. The model decides how to perform each task, but not which tasks exist or how they connect. This keeps the overall direction stable.

Task graphs also help with recovery. If a step fails, the graph knows where the agent should return. Without this structure, agents panic and start improvising.

Constraint engines: preventing bad decisions before they happen

A constraint engine evaluates the agent’s proposed action before execution. The system checks:

• Does the action fit the current state?
• Does it violate any business rules?
• Does it exceed resource or permission limits?
• Does the input look suspicious or incomplete?

If something looks wrong, the engine blocks the action and asks the agent to correct itself. This is critical in finance, operations, compliance-heavy industries, or any workflow that touches customer data.

The constraint engine becomes the safety net that keeps the agent from drifting into unwanted territory. It also reduces the reliance on “please be careful” prompts – a fragile approach at best.

Verifiable steps: trust, but verify

Every action the agent proposes should be checked against criteria that humans agree are correct. This could include:

• Schema validation
• External tool confirmation
• Cross-checking output against previous steps
• Sanity limits (no more than X attempts, no empty fields, no unexpected formats)
• Policy rules

When steps become verifiable, the system stops relying on the model’s self-judgment. Instead, it treats the model as a suggestion engine. The infrastructure decides what actually passes.

This transforms the agent from a “black box decision-maker” into a collaborator that must justify each move.

Why these patterns matter

Without guardrails, agents behave like interns on their first day – confident, creative, and unpredictable. With guardrails, they become assistants who understand their boundaries. And boundaries matter because real systems face:

• Compliance requirements
• Access rules
• Data sensitivity
• High error costs
• Tight integration with legacy workflows

A well-structured agent respects these constraints automatically. A free-floating one breaks them without noticing.

The future: safe autonomy, not full autonomy

Most companies don’t need agents that “act alone.” They need agents that:

• Take reliable, verifiable steps
• Handle routine tasks without oversight
• Ask for help when uncertain
• Never step outside defined boundaries

The next wave of AI development is not about giving agents more freedom. It’s about giving them clearer constraints. Guardrails, state machines, task graphs, and constraint engines create predictable behavior even as models become more capable. They let teams scale autonomy without sacrificing control.