How to Build Reliable AI Agents in 2025

Key Concepts:

• AI Agents: Automated systems leveraging large language models (LLMs) to perform tasks.
• LLMs (Large Language Models): AI models trained on vast amounts of text data, capable of generating human-like text, reasoning, and understanding.
• Deterministic Software: Software that produces the same output for a given input, predictable and reliable.
• Context Engineering: The process of crafting and managing the information provided to an LLM to ensure accurate and relevant responses.
• Workflows/DAGs (Directed Acyclic Graphs): A series of interconnected steps or tasks, often involving both code and LLM calls, to achieve a specific goal.
• Intelligence Layer: The component responsible for making the actual API call to the LLM.
• Memory: The ability to retain and utilize past interactions to inform current responses.
• Tools: External functions or APIs that the LLM can call to perform actions beyond text generation.
• Validation: Ensuring that the LLM's output conforms to a predefined structure or schema.
• Control: Using deterministic code (if/else statements, routing logic) to guide the flow of the application.
• Recovery: Implementing error handling and fallback mechanisms to ensure resilience.
• Feedback: Incorporating human oversight and approval steps to ensure quality and safety.
• Structured Output: LLM responses formatted in a predefined structure, such as JSON, for easier processing.

1. The Problem: AI Overload and Misdirection

• The AI space is oversaturated with information, tools, and frameworks, leading to developer confusion and anxiety.
• Many tutorials and resources are messy, contradictory, and focus on complex agent frameworks rather than foundational principles.
• The hype surrounding AI agents often overshadows the importance of traditional software engineering practices.
• The core issue is that many developers are distracted by the "noise" and don't focus on the fundamental building blocks.
• The speaker aims to provide clarity and help developers focus on the core principles for building reliable AI agents.

2. The Solution: Foundational Building Blocks and Software Engineering Principles

• The key to building effective AI agents is to understand and utilize seven foundational building blocks.
• The most effective AI agents are primarily deterministic software with strategic LLM calls.
• Developers should prioritize solving problems with code and only use LLMs when necessary.
• LLM API calls are expensive and potentially unreliable, so they should be minimized.
• Context engineering is crucial for getting accurate and reliable responses from LLMs.
• AI agents are essentially workflows or DAGs, with most steps being regular code, not LLM calls.

3. Seven Foundational Building Blocks

3.1. Intelligence Layer

• This is the core AI component where the API call to the LLM is made.
• It involves sending a user input to the LLM and receiving a response.
• Example: Using the OpenAI Python SDK to connect to the API, select a model, and send a prompt.
• The speaker emphasizes that the LLM call itself is straightforward; the complexity lies in the surrounding components.

3.2. Memory

• LLMs are stateless, meaning they don't remember previous interactions.
• Memory ensures context persistence by storing and passing conversation history to each interaction.
• Example: Storing a conversation history as a sequence of messages (user and assistant) and passing it to the LLM with each new input.
• In real-world applications, conversation history would be stored and retrieved from a database.

3.3. Tools

• Tools enable LLMs to interact with external systems, such as APIs, databases, and files.
• The LLM decides whether to use a tool based on the input and context.
• If a tool is selected, the code executes the tool and passes the result back to the LLM.
• Example: Providing the LLM with a "get weather" tool that can retrieve weather information for a given city.
• Tool calling is directly supported by major model providers, eliminating the need for external frameworks.

3.4. Validation

• Validation ensures that the LLM's output conforms to a predefined structure or schema.
• LLMs are probabilistic and can produce inconsistent outputs, so validation is crucial for quality assurance.
• If validation fails, the output is sent back to the LLM for correction.
• Example: Using Pydantic to define a JSON schema for task information (task, due date, priority) and validating the LLM's output against that schema.
• Structured output is essential for building reliable systems around LLMs.

3.5. Control

• Control involves using deterministic code (if/else statements, routing logic) to guide the flow of the application.
• This prevents the LLM from making every decision and allows for more predictable and reliable behavior.
• Example: Using an LLM to classify the intent of a user message (question, request, complaint) and then routing the message to the appropriate function based on the intent.
• The speaker prefers using structured output and intent classification over tool calls for complex systems, as it provides better debugging capabilities.

3.6. Recovery

• Recovery involves implementing error handling and fallback mechanisms to ensure resilience.
• APIs can go down, LLMs can return nonsense, and rate limits can be hit, so error handling is essential.
• Example: Using try/except blocks to catch errors and retry requests with backoff or provide fallback responses.
• This is standard error handling that would be implemented in any production system.

3.7. Feedback

• Feedback involves incorporating human oversight and approval steps to ensure quality and safety.
• Some processes are too complex or sensitive to be fully automated, so human review is necessary.
• Example: Requiring a human to review and approve an LLM-generated email before it is sent to a customer.
• This is a basic approval workflow with a human in the loop.

4. Orchestration and Workflow Design

• The speaker emphasizes the importance of orchestrating these building blocks to create complete workflows.
• The key is to break down a large problem into smaller problems and solve each sub-problem using the appropriate building blocks.
• LLM API calls should only be used when absolutely necessary.
• The speaker recommends checking out his workflow orchestration course for more information on how to combine these building blocks.

5. Conclusion

• By understanding and utilizing these seven foundational building blocks, developers can build reliable and effective AI agents.
• The key is to prioritize software engineering principles and minimize reliance on LLM calls.
• Context engineering, validation, and control are crucial for ensuring quality and reliability.
• Human oversight and feedback are essential for complex or sensitive processes.
• The speaker encourages developers to focus on these core principles and ignore the hype surrounding complex agent frameworks.