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

Join Sean, a machine learning engineer at K, as he shares insights on building scalable, safe, and seamless Enterprise LLMAgent solutions that tackle the challenges of observability, framework selection, and evaluation in this engaging talk.

1. Speaker is Sean, a machine learning engineer at K, discussing building enterprise LLM (large language models) agents.
2. Agents are the most exciting application of generative AI today, with growing demand in various sectors like customer support and financial analysis.
3. Building scalable, safe, and seamless LLM agents is challenging due to numerous frameworks, tools, models, and evaluation criteria.
4. Focus on three critical components when choosing a framework: observability, setup cost, and support.
5. High levels of observability are often required for building large-scale enterprise agents; consider using Langra or going native.
6. For quick tests and proofs of concept, use Frameworks like AI or Autogen, which offer low setup uplift and pre-existing agents/tools.
7. K continues improving integration support for various frameworks and expects the landscape to evolve.
8. When deciding on an approach/strategy: start simple with a single LLM and clear tool specifications for better performance.
9. Simplify input types, provide clear instructions, and limit chat history length to avoid model hallucinations.
10. Multi-agent style orchestration is growing in interest, requiring a good routing model, reasoning model, and well-constrained sub-agents.
11. Ensure the router contains clear descriptions and sharp routing instructions for potential edge cases.
12. Sub-agents should be constrained to performing independent tasks with a small set of tools.
13. Safety is paramount in scalable real-world applications; incorporate human-in-the-loop for critical business processes.
14. A golden set of ground truth user queries, expected function calls, and outputs helps assess an agent's performance.
15. Autonomous LLM agents tend to fail, so explore various failure mitigation strategies like prompt engineering, targeted annotation data sets, and synthetic data fine-tuning.
16. K is continuously improving base model performance, framework approaches, and developing a single container deployment called North for agentic applications.
17. North is a one-stop shop for using and building agentic applications with access to various databases, search capacities, and application connectivity.
18. North includes reasoning chains of thought, pulling relevant documents, providing breakdowns of tools called and outputs, and SQL-like style query retrieval from recent conversations.
19. Users can update specific tool calling capacities and ask the model to correct which tool call was used.
20. The talk aims to provide insights into deploying enterprise LLM agents and shares learnings packaged in North.

Source: AI Engineer via YouTube

❓ What do you think? What are the most significant challenges and opportunities in deploying Enterprise LLMAgent solutions, and how can organizations effectively mitigate risks and achieve successful implementations? Feel free to share your thoughts in the comments!