Building Scalable & Safe Enterprise LLM Agents: Frameworks, Strategies, Evaluation, and Failure Mitigation

As a machine learning engineer, I'll be discussing the critical components of building Enterprise LLMAgent solutions, including frameworks, approaches, evaluation criteria, and failure mitigation strategies, with a focus on insights gained from building agents at K.

1. Sean is a machine learning engineer at K, and he will be discussing building enterprise LLM (large language models) agents.
2. LLM agents are exciting with growing demand in various sectors like customer support, personal assistance, and financial analysis.
3. Building scalable, safe, and seamless LLM agents is challenging due to numerous frameworks, tools, models, and evaluation criteria available.
4. The critical decision-making process in setting up Enterprise agents involves focusing on observability, setup cost, and support.
5. Observability is crucial for debugging and fixing issues; high levels of observability are required for building large-scale enterprise agents.
6. For quick tests and proofs of concept, low-setup-cost frameworks like AI and Autogen are recommended.
7. Continuous improvement in integration support for various frameworks is expected at K.
8. Starting simple with a single LLM and being diligent about tool specifications can significantly improve performance.
9. Clear descriptions, sharp examples, and simplified input types help achieve performance gains.
10. Long streams of chat history (over 20 turns) can induce hallucinations in some models; caching relevant history can improve LLM agent performance.
11. Multi-agent style orchestration Frameworks like Autogen support collections of simple agents with a routing model for sub-agents.
12. A good routing model should contain clear descriptions and sharp sets of routing instructions to handle potential edge cases.
13. Sub-agents should be well-constrained, performing independent tasks with a small set of tools to return the final answer.
14. Safety is paramount for scalable real-world applications; incorporating human-in-the-loop feedback is essential in business applications.
15. Human-in-the-loop can be triggered under various criteria, before or after tool calls, ensuring safety and control.
16. Evaluation of LLM agents involves assessing their ability to make the right tool call at the right time, accurately passing input parameters, and reasoning effectively.
17. Intermediate stages leading up to the final response are crucial for developers during debugging and understanding decision-making processes.
18. Building a golden set of ground truth user queries, expected function calls, parameter inputs, outputs, and final responses helps assess critical points of failure.
19. Autonomous LLM agents have a tendency to fail; continuous exploration of failure mitigation strategies is essential.
20. Low severity/failure rate issues can be addressed with prompt engineering, while targeted annotation data sets help close performance gaps in specific tasks.
21. High failure rates may require building larger corpora using synthetic data and fine-tuning the model.
22. K is continuously improving base model performance at tool calling and developing a single container deployment called North for agentic applications.
23. North is a one-stop shop for using and building agentic applications, with access to various Vector DBs, search capacities, and connectivity to various applications like Gmail, Outlook, Drive, and
24. The demo of North showcases its ability to connect to Gmail, Salesforce, and G Drive, invoke reasoning chains, pull relevant documents, and provide breakdowns of tools called and tool outputs.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!