Technical Tuesday: 10 best practices for building reliable AI agents in 2025

At UiPath, we’ve been living the agentic mindset for a while. We don’t just build demos; we build agents that ship, scale, and survive real enterprise chaos.

If you’ve ever wired a large language model (LLM) into production, you know: it’s not the prompts that break. It’s everything around them. Error handling, context management, tool contracts, traceability. That’s why we built UiPath Agent Builder in Studio the way we did. We wanted to give you the control and observability you need to make AI agents work like real software components.

Here’s what we’ve learned building, testing, and shipping agentic automations at scale. These are the agent builder best practices that’ll help you go from “it kinda works” to “this thing runs in production without waking me up at 2 am”.

1. Design agents that fail safe (not just fast)

• Integrate agents thoughtfully within automations: avoid embedding agents inside a REFramework unless you have a very strong use case. Agents introduce variables (e.g., escalations, error handling) that must be carefully managed. Instead, UiPath Maestro™ is recommended for better visibility and control.

• Avoid retry mechanisms for agents: agent output isn’t deterministic, therefore retrying won’t guarantee improvement. Instead, capture and handle errors within the agent or tool itself.

• Start small and focused: begin with single-responsibility agents; each with one clear goal and narrow scope. Broad prompts decrease accuracy; narrow scopes ensure consistent performance.

• Modularize into multiple specialized agents: build modular systems by combining agents and robots for complex workflows instead of one “do-everything” agent. This allows controlled scaling, easier debugging, and flexible reuse.

• For deterministic tasks, use tools: bound risk by calling proven UiPath automations or APIs as tools rather than letting the agent directly act, when the use case demands it. This increases predictability and safety.

• Align agent goals and measurable outcomes: define clear objectives, performance metrics, and success criteria before design begins. Agents should operate within measurable boundaries.

2. Configure context the right way

• Index your enterprise context: index the structured sources, knowledge bases (KBs), and documentation your agent will rely on. Good planning and context setup are key to reliable execution. Make sure you choose the right search strategy. Semantic search finds meaning-based matches in unstructured text and structured search retrieves exact data from defined schemas. DeepRAG combines both to reason deeply across large, complex, or mixed sources.

• Choose the right model: UiPath Agent Builder in Studio is model-agnostic, therefore use the model best suited to your use case. GPT-5, for example, is generally more reliable than GPT-4. Use a different model for evaluation than for the agent itself to avoid bias.

• Maintain clarity in tool definitions: use simple, descriptive tool names with lowercase alphanumeric characters and no spaces or special characters. Names must match what’s referenced in the prompt exactly.

3. Treat every capability as a tool

• Treat every external capability as a tool: tools should have tight input/output contracts and clear success criteria. Reuse UiPath automations as tools whenever possible.

• Schema-driven prompts: keep tool prompts concise and structured. Validate output shapes and handle null or empty results explicitly.

• Document and version tools: maintain clear versioning and evaluation history per tool. Link evaluation runs to specific versions.

• Build tools to increase reliability of the agent for deterministic tasks: LLMs are not great at math, comparing dates, etc. In order to avoid any issues with the reliability of the agent, build tools that perform complex operations.

4. Write prompts like product specs (not prose)

• Iterative design and testing: prompt engineering is an iterative craft, so use UiPath Agent Builder to refine your system prompts and task instructions by building proper evaluation sets and testing as you build.

• Start with a system prompt that defines:

  • Role and persona

  • Instructions

  • Goal and context

  • Success metrics

  • Guardrails and constraints

• Use structured, multi-step reasoning: incorporate chain-of-thought style reasoning for complex workflows. Explicitly define task decomposition, reasoning methods, and output formats.

• Be specific and as detailed as possible about the desired outcome of your agent: make sure you define the proper output schema of your out arguments in UiPath Data Manager. Providing examples helps as well.

• Describe what should happen instead of what should not happen: It's the difference between prompting your AI agent with “Do NOT ask for personal information” and “Avoid asking for personal information, instead refer the user to…”.

• Consider different prompts to accomplish the same task: models have different implicit behavior. For example, the tendency to raise errors when uncertain, so they need specific per-model instructions.

• Use evaluation sets to help fine-tune the prompt: experiment with models and prompts with prompt optimization tools.

• Use Markdown language: using this language allows you to emphasize certain aspects in your prompt. Example: * *Critical:* *

• Avoid referencing input arguments in the prompt by their value: for example, {{input}}, because the value will be replaced at runtime with the actual argument value.

Want to expand your prompt skills? The UiPath Academy has you covered with the "How to write better prompts" and "Agentic prompt engineering" courses.

5. Evaluate for the real world

• Build robust evaluation datasets: have at least 30 evaluation cases per agent. Simulate tools and escalations that could block runs. Include success cases, edge cases, and failure scenarios.

• Evaluate for breadth and depth: cover multiple dimensions—accuracy of the outcome, reasoning, traceability, adaptability, and tool-use success.

• End-to-end testing: evaluate agents inside full automation contexts, not just in isolation. Test integration, communication, recovery, and failure modes.

• Use tracing: regularly review Trace Logs to inspect the agent’s reasoning loop, decisions, and tool usage. Identify errors, inefficiencies, and unexpected behaviors.

• Metrics and governance: track health score and regression metrics, and gate publishing on passing thresholds.

6. Build-in safety, governance, and compliance

• Run agents via UiPath Orchestrator or Maestro: deploy agents as processes to inherit lifecycle management, auditing, and governance.

• Leverage AI Trust Layer: apply per-group permissions, PII redaction, audit logs, throttling, and usage controls.

• Maintain a human-in-the-loop: use escalations for human review on high-risk decisions. These interactions inform agent memory, improving future runs.

• Use guardrails: set and enforce rules for acceptable behavior and escalation.

7. Version on purpose and gate releases

• Version everything: maintain clear version control for prompts, tools, datasets, and evaluations.

• Gate production release: move agents to production only after evaluations pass and rollout plans are finalized.

• Attach evaluations to version tags: ensure traceability from design to deployment.

8. Design conversations that build trust

• Set clear expectations: communicate what the agent can and can’t do. Provide transparent tool actions and clear human/robot escalation paths.

• Confirm irreversible actions: use deterministic confirmations (“I will create X with Y fields — proceed?”).

• Design for transparency: show context or reasoning snippets where appropriate to build trust.

9. Control cost and performance without sacrificing quality

• Optimize model usage: right-size your model choice (large models for complex reasoning, smaller ones for classification or routing).

• Limit token use: keep retrievals focused, summarize long contexts, and cache stable responses.

• Batch and tier operations: batch low-risk calls and escalate only when necessary to higher-capability models.

10. Improve continuously with traces, memory, and human feedback

• Trace and learn: use tracing and evaluation capabilities in Agent Builder to iteratively improve reliability. Use agent memory to help the AI agent learn from escalations resolved by people.

• Human feedback loop: escalations, evaluation feedback, and run logs should all feed back into design updates as well as agent memory.

• Scale incrementally: expand agent capabilities only after stability and performance are proven at smaller scale.

Ready to build your first production agent?

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FAQ: Agent Builder and AI agents

What is an agent builder?

An agent builder is a development environment that lets you design, configure, and deploy AI agents that can reason, decide, and act (safely and reliably) within your enterprise environment.

Why use UiPath Agent Builder instead of a generic LLM agent tool?

UiPath Agent Builder in Studio is designed for production, not prototypes. It combines scoring and evaluation-driven development for enterprise readiness with seamless integration into your existing business systems. You can tap into a full toolset, including UI automations and intelligent document processing (IDP), through a low-code, user-friendly interface, and scale effortlessly across the broader UiPath Platform for end-to-end automation.

How do I evaluate AI agents before production?

Use evaluation datasets, trace logs, and regression metrics to validate accuracy, tool use success, and safety. UiPath evaluation and tracing features make this simple and repeatable.

Can agents improve over time?

Yes. Agent memory and escalation feedback loops help agents learn from human intervention and evolve safely over time.