Developing Agentic AI: Lessons Learned & Pitfalls to Avoid
Lessons from the Trenches on What Works and What Doesn’t Work in Agentic AI
You built an AI Agent, but it has not been working as expected lately, and debugging the root cause feels like a massive challenge. You are frustrated and think,
“What if I had known and implemented the best practices for AI agents before I started?”
Was the mess avoidable?
Imagine a smoother path to building a robust AI Agent with best practices in mind to avoid frustration in the future.
Explore the best practices for AI agents that I figured out the hard way over the past few months — so you don’t have to learn the hard way.
I have been deep in the trenches of building AI agents, learning and developing, experimenting, making mistakes, and reworking different architectures of AI agents. Through this trial and error, I learned what works and what doesn’t work and the pitfalls to avoid.
If you want to know more about AI Agents, read the article below…
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Building A Simple Agentic Workflow with LangGraph
AI Agents Best Practices — Quick Recap
- Be clear on what you want your AI Agent to accomplish
- Keep it Simple and Explainable
- The Power of Focus: Single-Responsibility Agents
- Use no more than 4 -5 tools
- Build an Explainable AI Agent with Monitoring
- Plan Human Intervention and Oversight
- Clarity in Communication
- Define Guardrails or Safeguards
For a deeper dive into each best practice, read the full article or click on each hyperlink.
Best Practice 1: Be clear on what you want your AI Agent to accomplish
Define a clear objective for the AI agent and a comprehensive plan for achieving the goal.
The objective should define what you are trying to achieve, decisions on what tools should be used to achieve the goal, knowledge sources for information retrieval, and the need for short or long-term memory.
My goal was to create a Research AI Agent capable of providing the latest information on a given topic. This led me to consider several key questions:
- Core Logic: What topics should the Research agent query? Do I need the agent to focus on general subjects or specific areas like physics, data science, AI, etc.?
- Autonomy vs. Predefined Steps: Should the AI agent’s steps be predefined for more control, or should they operate autonomously, adapting their approach based on the task and available information?
- Latency Considerations: Understanding the latency considerations and LLM that will support the latency requirement. Depending on my use case, taking time to generate the research report was fine; however, if I wanted real-time answers or an audio response, then the response should be generated instantaneously.
- LLM selection: Which LLM can handle reasoning, planning, and latency requirements while also fitting the budget?
- Monitoring and Explainability: To what extent should each step of the AI agent’s process and steps be monitored and explained?
- Tool Selection and Limitations: What are the most suitable tools to accomplish the task that I wanted to consider, like web search, YouTube search, academic paper search, etc.? What are their availability, costs, latency, etc.?
- Interactive Refinement: Beyond simple research output, should the agent’s responses be dynamically adjusted through interactive conversations to refine the results? Does the agent need to assume any specific personality?
- Output Quality and Purpose: Should the agent produce general-purpose research output or prioritize high-quality, specialized research results?
- Error Handling and Recovery: How will the AI Agent handle errors or unexpected conditions? What mechanism needs to be implemented for error detection and recovery?
- Ethical Considerations and Guardrails: What are the ethical considerations for AI agents when retrieving data from different sources, processing data, and producing final output? What guardrails need to be implemented for the AI Agent to function safely, ethically, and reliably?
Best Practice 2: Keep it Simple and Explainable
AI Agent should always be designed with simplicity and explanablity as a core principle to build trust and transparency in its actions and decision-making process
AI agents should always be transparent in their decision-making process and explain the reasons for their actions. Select the tool that accomplishes the goal of building trust, facilitating debugging, and understanding the reason for adapting to changes in data and environment.
Best Practice 3: The Power of Focus: Single-Responsibility Agents
Agentic AI systems must divide complex tasks into smaller, manageable ones. Each AI agent should specialize in a single task and collaborate with other AI agents to accomplish complex tasks.
When agents have a clear purpose, its decisions and actions become more predictable and interpretable, leading to easier debugging and better explanablity
Challenges with Multipurpose AI Agents
Inconsistent Behaviour: Agents with multiple responsibilities often face competing goals, which can result in decision paralysis or inconsistent behavior. For example, a customer service AI agent that aims to resolve customer issues quickly while ensuring high customer satisfaction has competing goals, which can result in inconsistent behavior.
Security Risks Amplified: As the scope of the multi-purpose agent increases, it will also need broader permissions to operate, thus increasing potential security vulnerabilities.
Dependency Disasters: Just like non-modular software, an AI agent with multiple responsibilities can become difficult to manage. When one functionality needs an update, dependencies can create challenges, making modifications complex and error-prone.
Best Practice 4: Use no more than 4 -5 tools
Tools enable AI Agents to take action to accomplish their goals.
Fewer than 4–5 tools available to AI Agents simplify their decision-making process and enhance their reliablity.
For the research Agent, I used three tools: a web search using Tavily, an ArXiv search for academic papers, and a YouTube search for relevant videos on the searched topic. This was good for general research; however, when I added tools for specialized financial and economic research, the project encountered significant challenges. The agent code became more complex, the results were not reliable, and responses took more time to generate.
Reduced complexity and increased reliability: More tools confuse AI agents and increase the chance of using the tools incorrectly, producing incorrect results.
Minimized Attack Surface: Each tool represents a potential vulnerability. Limiting the number of tools reduces the attack surface, making it harder for malicious actors to exploit the agent.
Optimized Performance: Fewer tools mean less code, fewer dependencies to manage, and faster execution. Agents will spend less time deciding which tool to use, leading to lower resource consumption.
Use Open Standards like Model Context Protocol(MCP) to connect AI Agents to different tools
Best Practice 5: Build an Explainable AI Agent with Monitoring
Monitoring involves continuously tracking an AI agent’s behavior, action, performance, and resource usage in real-time or near real-time. It aims to detect anomalies, errors, and deviations from expected behavior.
Explainability refers to the ability to understand, explain and make AI agent’s reasoning, decision-making process and actions transparent.
- Continuously logging an AI agent’s actions, inputs, outputs, and errors allows one to monitor whether the agent is managing its resources efficiently.
- Implement health checks to identify and diagnose errors, bugs, and unexpected behavior.
- Perform frequent audits and tracking to identify security breaches and inconsistent and malicious behavior.
- Providing a step-by-step trace of the agent’s decision-making process helps with explainability to build transparency and trust.
Best Practice 6: Plan Human Intervention and Oversight
Defining boundaries for AI agents to operate is crucial to ensure ethical decision-making.
Build Human oversight or intervention to ensure that the AI agent is operating within appropriate boundaries by randomly reviewing AI agents actions and decison-making.
Build a mechanism for human intervention when an AI Agent faces edge cases or is unclear on decision-making. You can also consider human-based approvals in cases of financial approvals over a certain amount or content published to external parties like high-value customers, the press, etc.
Best Practice 7: Clarity in Communication
Clear Communication is crucial for AI Agents and for any objective we are trying to accomplish where decisions need to be made, and collaboration is required.
- Communication between different components of the Agent: What attributes are required to maintain the state of the AI Agent so that it can effectively communicate and coordinate different components? When using tools for the Research agent, I had to store the results from respective tools before they were summarized by LLM as a final output.
- Clear instructions to LLM and tool calling: Provide clear, unambiguous instructions to LLM based on the expected outcome for ensuring the right tool calling. Providing clear prompts with proper context, expected outcomes, or any role or personality the LLM needs to assume will result in better responses from the AI agent.
Best Practice 8: Define Guardrails or Safeguards
Guardrails along the edge of the road prevent vehicles from going off the road and endangering lives. Similarly, we need robust safeguards for AI agents.
Guardrails in AI Agents are safeguards designed to ensure agents operate in a safe, responsible, ethical, fair, transparent and unbiased manner.
Guardrails must possess adaptive fail-safe characteristics, enabling them to intervene when agents exhibit potentially harmful behavior. A continuous feedback mechanism is essential to achieve this, allowing guardrails to be refined over time based on human oversight and audits of autonomous AI Agents.
Effective AI agent guardrails should address the following key areas:
- Privacy: Protecting sensitive and PII data by monitoring for data leakages in input prompts, intermediate results, and final outputs.
- Security: Implement a comprehensive security framework to prevent malicious activities such as jailbreaks. AI agents should access tools and APIs using the principle of least privilege, giving only the minimum required access to accomplish the goal.
- Compliance or Regulatory Checks: Implement use-case and industry-specific checks to maintain compliance with relevant regulatory standards.
- Logging: Record all steps taken by AI Agents, including planning, reasoning, decision-making, and tool usage for auditing and debugging.
- Monitoring: Continuously monitoring and auditing agent behaviors to intervene in a timely manner when AI agents potentially deviate from their intended path.
By implementing these safeguards, you can reduce the undesirable and unsafe behavior of AI Agents.
Conclusion:
Whether pre-defined or autonomous, AI agents are our world's future. Implementing best practices for developing AI agents becomes crucial as we develop and deploy complex, autonomous AI agents. The best practices will ensure transparency in reasoning, planning, and decision-making. Ethical, responsible, secure, fair, and unbiased behavior that generates trust and reliability. Responses generated by AI agents are fast, fact-based, and relevant to their objective.
References:
https://www.salesforce.com/blog/responsibly-manage-multi-agent-systems/
