Challenges and Solutions for Deploying LLM Agents in Production

These days LLM agents have become incredibly powerful tools, capable of handling a wide range of tasks, from generating human-like text to automating complex workflows. I've been captivated by the power and capabilities of these agents. However, in my experience, using agents for specific tasks and building a fully functional agentic workflow are two very different things.

One of the main challenges is deploying agents seamlessly into production. Moving these agents from development to a production environment is not straightforward at all. Despite their immense potential, LLM agents often face issues that can hinder their performance and reliability.

In this article, we'll explore some of the primary problems I've encountered when deploying LLM agents into production and offer solutions to overcome these challenges.

Reliability is a concern.

Reliability is crucial for any production system, and LLM agents are no exception. LLM agents often fall short when it comes to reliability because they are still a relatively new technology. This gap makes us hesitant to rely solely on these agents for critical tasks.

Recently, a colleague asked me about deploying CrewAI into production. My response was that I wouldn't currently deploy CrewAI into production because it has so many issues that I can't trust it. Instead, I would use LangGraph in production because it's much more reliable. However, CrewAI can be used to build some of these crews really quickly and try out ideas and get a sense of what is probably going to work and what is not going to work.

Agents can sometimes produce unexpected or incorrect outputs, especially in edge cases or unfamiliar contexts and this is risky because in a production environment consistent and accurate results are essential.

Solution

• Implement robust testing frameworks and cover a wide range of scenarios, including edge cases.
• Incorporate fallback mechanisms such as escalate issues to a human operator when necessary.

Excessive Loops can be costly.

Another common issue with LLM agents is their tendency to enter excessive loops. This happens when the agent repeatedly attempts the same task without making progress, either due to a failure in an external tool or because it doesn't find the generated output satisfactory.

Excessive loops aren't a big deal if you are using open source LLMs like Llama but imagine using GPT-4o for API calls and the agent keeps calling repeatedly and unnecessarily and you end up getting a massive bill. Be mindful, because these loops can lead to increased costs and inefficiencies, particularly when using expensive models or APIs.

An agent might be designed to gather information from various sources and if one of the sources fails to provide the needed data, the agent might keep retrying indefinitely, consuming resources without achieving the desired result.

Solution

• Implement strict limits on the number of retries or steps an agent can take for a given task to address excessive loops - utilize frameworks like LangGraph and CrewAI, which offer features to set these limits.
• Conduct regular monitoring and logging to provide insights into the agent's behavior - identify looping issues early through continuous observation.

Existing Tools may not be the best.

Tools play a critical role in the functioning of LLM agents, but they can also be a source of problems. Many existing tools, especially the No Code Tools and those bundled with frameworks like LangChain, are designed for general purposes and may not be well-suited for specific agent tasks. These tools might be outdated or lack the necessary functionality, leading to suboptimal performance.

For example, CrewAI is built on top of LangChain and provides an intuitive easy-to-use API. On my recent projects, I transitioned from using LangChain by developing smaller frameworks focused on specific tasks. This made it incredibly easy to create new tools for the agents. All you need to do is write a Python function and use a decorator to mark it as a tool. One of the tools I've developed allows me to add sub-agents for specialized tasks. The model's instructions inform the primary agent about these sub-agents and explain how they can assist in performing tasks.

Solution

• Invest time in creating your own intelligent tools that can adapt to different contexts and provide meaningful feedback to the agent.
• Develop specialized tools that handle data inputs and outputs. Ex: tools capable of managing data transformations, filtering, and error handling.

Validation is a must.

This involves validating outputs against expected results, so validation mechanisms are essential for accurate and reliable outputs. Without these mechanisms, agents can generate incorrect results, leading to failures in their tasks.

For instance, if an agent is designed to write code, it should have a way to run unit tests on the generated code to ensure it functions as intended. Similarly, for tasks involving data retrieval, as the agents' underlying LLMs can hallucinate, the agent should provide the source in which the data is retrieved to ensure its accuracy.

Solution

• For coding tasks, implement automated testing frameworks that run unit tests on the generated code - ensure that the tests cover a wide range of scenarios, including edge cases, to catch any potential errors.
• For non-code tasks, design validation mechanisms that check the accuracy and relevance of the outputs - this can involve verifying the existence of URLs, retrieve data with trusted sources, or performing sanity checks on the outputs.

Lack of Explainability.

Have you heard of black box ML models? This is the equivalent black box version when it comes to agents. As a user, I need to understand why an agent made a particular decision or produced a specific output. Without clear explanations, users may be skeptical of the agent's reliability and hesitant to adopt it for important tasks.

For example, if an agent provides a recommendation, users should be able to see the underlying rationale, such as the data sources used, and the criteria applied. When generating reports based on multiple sources, include a list of these sources with relevant citations. Use standardized citation formats to ensure consistency and clarity.

Solution

• Provide clear citations and references for the information used in making decisions.
• Develop logging mechanisms of decisions taken.

Debugging an Agent in Production.

Debugging LLM agents can be particularly challenging due to the complexity of their operations. Since there is limited access to most production environments, without clear logs and debugging mechanisms, it can be difficult to identify where and why an agent is failing.

For example, if an agent encounters an error while processing data, detailed logs can help pinpoint the exact step and data involved, making it easier to diagnose and resolve the issue.

Solution

• Develop comprehensive logging and debugging tools.
• Implement monitoring systems.

In conclusion, deploying LLM agents into production environments requires solving several important problems, such as ensuring reliability, avoiding excessive loops, customizing tools, implementing self-checking, making them explainable, and debugging.

By addressing these issues step by step, we can improve the performance and reliability of their LLM agents, leading to wider use and more advanced applications.