LLMs Are the Smallest Part of Your AI Agent System
What happened
Discussions about AI agents often overemphasize the large language model (LLM) component, but production-ready agentic applications require a significant shift in focus to the surrounding infrastructure. Building reliable AI agents necessitates prioritizing the 'harness' around the model, managing persistent state, and implementing effective checkpointing for reliability.
Why it matters
AI Engineers building production-ready agentic systems must prioritize designing robust runtime harnesses, managing persistent state, and implementing effective checkpointing, as these infrastructure components are more critical for reliability than the LLM itself.
Topics
- AI Agents
- LLM Architecture
- Runtime Harness
- Persistent State
Articles in this trend
- Why LLMs Are the Smallest Part of Your AI Agent System👨🔧 — Turing Post
- Minimum Viable Model: Structured Model Selection Criteria For Agents — High ROI AI
- How building software is changing at Anthropic — The Pragmatic Engineer
- LAI #136: Build Faster With Agents, Debug Their Failures, and Evaluate Them More Reliably — Learn AI Together
- The Anatomy of a Production AI System — DataJourney
- Human-Centric Methodologies In AI Reliability By Mayank Vadaliya — HackerNoon
- What Optimizely Customer Zero Teaches About Agentic AI Governance — Featured Blogs - Forrester
- Your Company Doesn’t Need Another AI Agent. It Needs a Better Workflow. — AI on Medium
- The data scientist is dead — long live the data science conductor — CIO
- When the Code Becomes the CEO: Why Your Next Manager Might Be a Decentralized Agentic Loop — Towards Data Science
- Measuring the Tendency of AI Agents to Go Rogue — Schneier on Security