EP 545: How to build reliable AI agents for mission-critical tasks

Unknown Source June 12, 2025 28 min
artificial-intelligence generative-ai startup investment google
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23 Companies
52 Key Quotes
4 Topics

🎯 Summary

Summary of Everyday AI Podcast Episode: Building Reliable Agentic AI for Mission-Critical Tasks

This episode of the Everyday AI Show, featuring Yash Sheth, Co-founder and COO of Galileo, focused on the urgent industry sprint toward agentic AI and the critical, yet undefined, playbook for ensuring these agents are reliable and trustworthy for mission-critical enterprise tasks.

1. Main Narrative Arc and Key Discussion Points

The conversation established that while the excitement around AI agents is high—driven by the potential for end-to-end automation without a human in the loop—the industry lacks a standardized playbook. The core challenge discussed was bridging the gap between powerful Large Language Models (LLMs) and deploying them reliably in enterprise systems that handle sensitive, dynamic data and real-world actions. The discussion moved from defining agents versus chatbots to examining current enterprise adoption, and finally, focusing on the necessary foundations for reliability (testing and evaluation) before scaling to multi-agent systems.

2. Major Topics, Themes, and Subject Areas Covered

  • Agentic AI Implementation: The current race by businesses to implement agentic workflows.
  • Reliability and Trust: The paramount importance of trust and reliability for agents operating on mission-critical tasks.
  • Enterprise Adoption Curve: The “crawl, walk, run” analogy for AI adoption, where current chatbot deployments are the “crawl,” productionized agents are the “walk,” and multi-agent systems are the “run.”
  • Agent Definition: Distinguishing agents (which involve planning, action, and feedback/deflection loops) from traditional chatbots (which typically end with an answer).
  • Multi-Agent Systems: The future architecture involving small, specialized agents communicating to complete complex workflows (e.g., travel planning, supply chain management).

3. Technical Concepts, Methodologies, or Frameworks Discussed

  • Non-Deterministic Software: The fundamental shift enterprises must adapt to, as agents introduce variability that traditional deterministic software did not have.
  • Test-Driven Development (TDD) for Agents: The necessity of applying software engineering best practices, specifically creating unit tests and integration tests (evals) tailored to the agent’s specific use case and expected metrics.
  • Agent Evals: The process of creating high-quality evaluations that go beyond standard LLM benchmarks and focus on tool selection quality and action correctness.
  • Tool Calling/MCP: Mention of the Model Communication Protocol (MCP) and Google’s A2A protocol as emerging standards simplifying how agents interact with tools and each other.
  • Galileo Agent Leaderboard: A practical tool showcasing real-world LLM performance across agentic prototypes using specific datasets focused on tool selection quality.

4. Business Implications and Strategic Insights

  • ROI Extraction: Agents are the key to unlocking the true Return on Investment (ROI) from LLMs by automating workflows beyond simple assistance.
  • Architectural Shift: The industry is moving from microservice architectures to micro-agent architectures, where software components become smarter and more independent.
  • Regulated Industries: Even highly regulated sectors (Finance, Healthcare) are rapidly moving toward productionizing agents this year, indicating widespread confidence in emerging reliability frameworks.
  • Mission-Critical Examples: Successful deployments include agents preempting internet outages, managing data platforms, and automating supply chain ordering.

5. Key Personalities, Experts, or Thought Leaders Mentioned

  • Yash Sheth (Co-founder & COO, Galileo): The primary expert guest, providing insights based on Galileo’s work supporting hundreds of enterprise agent development teams.
  • Jordan Wilson (Host, Everyday AI): The host driving the conversation.
  • Google Gemini Team (via sponsor message): Mentioned in the context of supporting the podcast and promoting their new video generation model, Veo.
  • The next phase of AI adoption will be multi-agentic systems (“run” phase).
  • The industry will converge on open protocols (like those being championed by the Agency Organization, which Galileo helped found) to ensure seamless communication between heterogeneous agents built on different LLMs.
  • The answer to “which model is best” is dynamic and must be determined via real-world agent leaderboards, not static academic benchmarks.

7. Practical Applications and Real-World Examples

  • Preempting Internet Outages: Agents monitoring infrastructure to prevent failures.
  • Data Platform Management: Agents autonomously managing organizational data systems.
  • Supply Chain Automation: Agents monitoring warehouse inventory and automatically placing orders.
  • Travel Booking: A future multi-agent scenario where one agent plans the itinerary and coordinates with specialized agents for flights, hotels, and restaurant reservations.

8. Controversies, Challenges, or Problems Highlighted

  • Lack of Playbook: The primary challenge is the absence of a standardized, official playbook for building reliable agents quickly.
  • Non-Determinism: The inherent unpredictability of LLMs makes building reliable systems that interact with real-world systems inherently risky.
  • Trust in Multi-Agent Handoffs: In multi-agent scenarios, agents must solve for trust, authentication, and clear communication protocols when handing tasks between specialized bots.

9. Solutions, Recommendations, or Actionable Advice Provided

  • Prioritize Single-Agent Reliability: Before tackling complex multi-agent systems, organizations must master the CI/CD pipeline and reliability stack for individual agents.
  • Implement Strong Evals: Treat agent development like traditional

🏢 Companies Mentioned

GitHub âś… tech
Agency Organization âś… unknown
World Cup âś… unknown
Google AI Pro âś… unknown
Did I âś… unknown
Google Gemini âś… unknown
Because I âś… unknown
And I âś… unknown
Yash Sheth âś… unknown
Everyday AI âś… unknown
Jordan Wilson âś… unknown
So I âś… unknown
Everyday AI Show âś… unknown
Agency Organization 🔥 organization
Hugging Face 🔥 tech

đź’¬ Key Insights

"good evaluations followed by good reliable preventions and mitigations is going to be absolutely critical to be successful in the world of non-deterministic software."
Impact Score: 10
"in that world of multi-agent existence, there are three things that need to be really solved for. First is again, trust. When an agent is talking to another agent, how can it trust that other agent?"
Impact Score: 10
"reliability in agents comes through a foundation of really test-driven development for these agents and having high-quality evals for an agent."
Impact Score: 10
"we are entering a world of non-deterministic software. And enterprises as a whole need to adapt to a world of non-deterministic software."
Impact Score: 10
"right now is trust and reliability. How can we make sure these when these agents are on mission-critical tasks, these agents have access and control over real-world systems?"
Impact Score: 10
"Enterprises as a whole need to adapt to a world of non-deterministic software. It's a new world. None of us have built these agents at scale before, and so reliability, setting up a reliable pipeline for building, shipping, and scaling these agents is absolutely critical..."
Impact Score: 10

đź“Š Topics

#artificialintelligence 66 #generativeai 9 #startup 5 #investment 3

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Generated: October 05, 2025 at 10:20 AM