Elevate Your AI: 10 Essential Best Practices for Building Reliable Agents in 2025

Here’s what we’ve discovered in our journey of creating, testing, and scaling automated agents. If you’re striving to elevate your automation game, these best practices will bridge the gap between "it kind of works" and "this operates seamlessly, without interrupting my sleep."

Design Agents for Safety First

When building automated agents, it’s crucial to prioritize their fail-safe design over mere speed. Here’s how to do it effectively:

Cut Out Retry Mechanisms: Since agent outputs can be unpredictable, retrying outputs isn’t likely to provide better results. Instead, focus on capturing and managing errors directly within the agent.
Start Small: Concentrate on agents that have specific, focused tasks. This narrow scope not only increases accuracy but also ensures that the agent performs consistently well.
Embrace Modularity: Instead of creating a single "super agent," opt for multiple specialized agents. This modular approach allows for easier debugging, controlled scalability, and greater flexibility.
Align Goals with Metrics: Clearly define what success looks like for your agents. Set measurable objectives before diving into design to ensure they work within established boundaries.

Configure Context with Care

Having the right context is vital for your agent’s success.

Index Relevant Information: Curate structured sources, knowledge bases, and documentation that your agent will utilize. A strategic setup not only supports reliable execution but also enhances the effectiveness of semantic and structured searches.
Simplify Tool Definitions: Use straightforward and descriptive names for tools that match exactly what’s referenced in prompts. This clarity will streamline the interaction and enhance performance.

Treat Capabilities as Powerful Tools

Every feature of your automation should be seen as a tool to enhance functionality.

Schema-Driven Prompts: Keep prompts organized and concise, validating outputs and handling null values. This structured approach boosts clarity.
Document Everything: Maintain rigorous version control for each tool and document their evaluation history. Linking evaluations to specific versions fosters a clear understanding of changes.
Build Reliable Tools: For tasks demanding precision, construct tools designed for reliable operations. For instance, if mathematical calculations are involved, don’t leave it to chance—create specialized tools for those tasks.

Craft Prompts Like Product Specifications

Writing effective prompts isn’t just about creativity; it’s about clarity.

Define Expectations Clearly: Begin with a system prompt that outlines the role, instructions, goals, context, and metrics for success.
Use Structured Reasoning: Incorporate chain-of-thought reasoning, especially for intricate workflows. Break down tasks into manageable steps for better clarity.
Encourage Positive Actions: Phrase prompts to encourage desired behaviors. Instead of saying, “Do NOT ask for personal information,” try “Avoid asking for personal information; instead, refer the user to…”
Experiment with Variations: Since models behave differently, craft multiple prompts to achieve the same result and refine based on outcome differentials.

Real-World Evaluation

Evaluating agents before deploying them is crucial for practical performance.

Create Comprehensive Datasets: Develop at least 30 evaluation cases per agent. Incorporate simulated tools and various scenarios to cover potential risks and edge cases.
Assess Across Dimensions: Measure the breadth of capabilities including accuracy, adaptability, and tool usage success.
Conduct End-to-End Testing: Evaluate agents within their complete automation contexts, ensuring smooth integration and communication, as well as recovery from failures.

Build-in Safety, Governance, and Compliance

As AI technology advances, implementing solid governance and safety measures is imperative.

Utilize AI Trust Layers: Enforce group permissions, manage sensitive information with redaction, and maintain thorough audit logs.
Implement Human Oversight: For high-stakes decisions, make sure a human review process is in place. This not only enhances safety but also informs the learning process of the agent.
Set Guardrails: Establish clear rules about agent behavior and escalation processes to maintain compliance and ethical standards.

Purposeful Versioning and Release Management

Taking a strategic approach to versioning and releases is essential.

Version Everything: Keep meticulous records of prompts, tools, datasets, and evaluations.
Gate Production Releases: Transition agents to production only after thorough evaluations have been passed.
Traceability: Attach evaluations to version tags to ensure a clear line from design to deployment.

Foster Trust with Conversational Design

Creating a trustworthy interaction environment is key to user acceptance.

Communicate Transparently: Clearly explain what the agent can do, and facilitate straightforward paths for human escalation.
Confirm Actions: For significant actions, ensure that confirmations are specific and direct.
Promote Transparency: Whenever possible, share reasoning with users to cultivate trust and understanding.

Balance Cost and Performance

Maintaining quality while keeping costs manageable is an ongoing challenge.

Optimize Model Choice: Choose the right model size based on the task complexity—larger for intricate reasoning and smaller for simpler classifications.
Limit Token Usage: Be cautious with how many tokens you utilize. Keep retrievals focused and cache stable responses when possible.
Batch Operations: Group lower-risk tasks and engage higher-capacity models only when necessary.

Commit to Continuous Improvement

Making enhancements should be an ongoing process driven by data.

Utilize Tracing and Evaluation: Monitor tracing data to identify areas for improvement. Allow your agent to learn from past interactions and problems.
Encourage Human Feedback: Utilize insights from escalations to refine design and enhance agent memory.
Scale Thoughtfully: Only expand agent capabilities after confirming that performance is stable at smaller scales.

Are you ready to embark on your journey of building an operational agent?

Get started with Agent Builder or see a live demo.

For developers, we have consolidated all the information you need regarding building and deploying agents into one convenient location. Start here.

If you’re wondering about the intricacies of agent building and AI integration, we’re here to help.

What is an agent builder?
An agent builder is an innovative development environment that empowers you to design, configure, and deploy AI agents capable of reasoning, making decisions, and acting safely within your enterprise landscape.

How do I evaluate AI agents before production?
Ensuring thorough evaluations before deploying agents is essential for their effectiveness.

Can agents improve over time?
Absolutely! With the incorporation of agent memory and feedback loops, agents learn from human interactions and evolve continuously, ensuring safe and effective performance moving forward.