AI agents are becoming increasingly sophisticated, capable of automating workflows, making decisions, and integrating with external tools. However, deploying AI agents in real-world applications presents significant challenges that can impact reliability, performance, and accuracy. Prioritizing a strong foundation in AI agent design today will set the stage for reliable and secure autonomous systems in the future.

This guide explores five of the most common hurdles developers face when creating AI agents—along with practical solutions to overcome them. Whether you're a beginner looking to get started or a seasoned developer refining your approach, these best practices will help you design AI agents that are more reliable, scalable, and effective in complex environments.

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1. Managing tool integration

As AI agents become more sophisticated, managing their access to and use of various tools becomes increasingly complex. Each additional tool introduces new potential points of failure, security considerations, and performance implications. Ensuring agents use tools appropriately and handle tool failures gracefully is crucial for reliable operation.

To address this challenge, create precise definitions for each tool in your agent's toolkit. Include clear examples of when to use the tool, valid parameter ranges, and expected outputs. Build validation logic that enforces these specifications, and start with a small set of well-defined tools rather than many loosely defined ones. Regular monitoring will help you identify which tools are most effective and where definitions need refinement.

2. Managing model reasoning and decision-making

A fundamental challenge in building AI agents is ensuring consistent and reliable decision-making. Unlike traditional software systems that follow explicit rules, AI agents must interpret user intent, reason about complex problems, and ultimately make decisions based on probability distributions. This non-deterministic nature makes it difficult to predict and control how agents will respond across different scenarios, especially in complex business environments.

To address this challenge, your organization can implement structured prompting approaches like ReAct, which provides a framework for systematic reasoning. Combining this with clear guardrails and validation checkpoints helps ensure reliable outputs. You can also implement more structured action paths with tools like Langchain and Llama Index.

LLM temperature settings play a key role in shaping a model's reasoning and creativity. These parameters control the randomness of the text that LLMs generate. Lower settings (close to 0) ensure precise, reliable outputs, while higher values (up to 1) introduce more variability and creativity. increasing the number between 0 and up to 1 allows for increased randomness in the next word selected (and, in turn, creativity). Experimenting with different settings can help you strike the right balance between creative problem-solving and predictable results. Based on our experience, a temperature between 0 and 0.3 works best for AI agent model calls. The more exact and predictable we need our model call to be, the lower the temperature we should use.

3. Handling multi-step processes and context

Complex enterprise workflows often require agents to maintain context across multiple steps and interactions. As these processes become more intricate, it’s increasingly challenging to manage state, handle errors, and maintain coherent context. Agents must track progress, understand dependencies between steps, and gracefully handle interruptions or failures at any point in the process.

The solution is to implement robust state management systems and clear validation checkpoints throughout multi-step processes. Build comprehensive error handling for each step of complex workflows, and design fallback mechanisms for when agents encounter unexpected situations.

For example, say a mortgage loan application agent is pulling a credit report from the last week, and tries three credit bureaus in sequence (Experian, Equifax, then TransUnion). If all fail, it searches for recent credit reports from the last 90 days before finally routing to a loan officer for manual handling. If at any point the agent encounters results in an unexpected format, it would immediately route those results to the loan officer for review.

In addition to ensuring error handling and fallbacks, be sure to clearly document process flows and implement logging systems to track the progression of multi-step tasks. This structured approach ensures agents can maintain context and recover from interruptions effectively.

4. Controlling hallucinations and accuracy

AI agents can sometimes generate plausible but incorrect information, particularly when dealing with complex queries or incomplete data. These hallucinations pose a significant risk in enterprise or public sector environments where accuracy is crucial. The challenge is particularly acute when agents are making decisions that impact business operations, customer interactions, or citizen services.

Combat this by implementing rigorous validation systems, leveraging grounding and citations, and using structured data formats like JSON to constrain response formats. Embed human review processes for critical decisions, and create comprehensive testing suites to catch potential hallucinations. Regular monitoring and logging of agent outputs can help identify patterns of inaccuracy and suggest improvements to the system. Consider implementing confidence scores and establishing thresholds for when to escalate to human review.

5. Performance at scale

Running complex AI agents in high-traffic production environments introduces a new class of engineering and operational challenges that aren't apparent during development or initial deployment. Cascading failures from tool timeouts and failures, incorrect responses, and resource bottlenecks from model serving and inference can quickly degrade system performance as request volumes increase.

Quick win fixes

Address these challenges by:

Implementing robust error handling at every tool integration point, with circuit breakers to prevent cascading failures.
Building retry mechanisms for failed tool calls, and maintaining a response cache to reduce duplicate model calls.
Implementing a queue management system that controls the rate of model calls and tool usage for handling concurrent requests.
If there is a human review step in your agent, utilize citations in model outputs to verify where responses came from.
Setting up LLMOps and other monitoring tools specifically focused on catching common failure patterns by tracking tool timeout rates, model response accuracy at scale, and system latency under load. This data will help you identify bottlenecks before they impact users, and adjust your rate limits and scaling policies accordingly.

Building AI agents that are reliable, scalable, and effective in real-world applications requires more than just deploying a large language model. From managing tool integrations and structured reasoning to handling multi-step processes and preventing hallucinations, each challenge presents unique hurdles that must be systematically addressed. By implementing robust validation mechanisms, leveraging structured prompting techniques, and incorporating fail-safe measures, organizations can significantly improve the performance and reliability of their AI agents.

For CTOs and CDOs leading AI initiatives, these best practices are not just technical optimizations but essential steps to ensure AI agents deliver real business value.

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Building AI agents: 5 common hurdles and fixes