#ai-agents
344 episodes · Page 2 of 15
#2690: Where Agent Builders Actually Gather
The MCP community, A2A protocol, and Linux Foundation are building the professional identity of agentic AI right now.
#2689: Why CLI Beats MCP for AI Agents Sometimes
Why a plain command-line tool can outperform a purpose-built MCP server for AI agents — and what that means for the protocol's future.
#2687: When Pre-Flight Checks Help (or Hurt) Agentic AI Plugins
How to decide when a pre-flight check is worth the latency cost — and how to write good ones.
#2685: Plugin Data Storage for AI Agents
How to separate user data from plugin code across Linux, macOS, and Windows in agentic AI environments.
#2684: When Agent Skills Collide: Context Windows & Plugin Design
How to handle overlapping agent skills and whether context windows will ever make the problem go away.
#2683: MCP vs Agent Skills: Context Wars
When 12M token windows arrive, do MCP servers or agent skills win? Plus: federated access for agent teams.
#2682: Live Retrieval vs. RAG: What an Agent Actually Does
Does every AI conversation create a tiny vector store? We unpack the real tradeoffs between live document fetching and pre-indexed RAG.
#2677: Memory Layers for AI Agents: SaaS vs Self-Hosted
Zep, mem0, Letta, Graphiti, Cognee — which memory layer should you commit to for your AI agent?
#2675: When AI Makes Documentation Effortless
The key documents every consultant needs—and how AI makes them effortless to create and maintain.
#2674: Why Your Agent's Context Window Is Getting Eaten Before You Start
Stop shipping the whole toolbox to every session. A bridge plugin pattern that fetches skills on demand instead.
#2673: The Embedding Coupling Problem: Editing Vector Stores
Can you edit or delete individual chunks in Pinecone? And can you actually back up a vector index? Yes—but with critical caveats.
#2638: How to Build Disposable AI Agents at Runtime
Create ephemeral AI agents that answer questions about specific items, then vanish. No persistent configuration needed.
#2602: Letting Non-Experts Direct Audio Tools Through Conversation
How to use AI for podcast mastering — and why agentic AI works better for small tasks than big promises.
#2551: How Progressive Disclosure Saves MCP from Token Bloat
Why dumping all tool schemas into context breaks accuracy — and three implementations that fix it.
#2541: Agent-to-Agent Scheduling: Building the Calendly for AI
How Google's A2A protocol and Anthropic's Remote MCP could power a new kind of agent handoff for scheduling meetings.
#2540: Does Your AI Framework Change the Output?
Same model, same prompts, different harness. Does the plumbing change the water?
#2535: Inside LangChain's Deep Agents: What's Actually in the Box
A deep dive into the batteries-included agent harness with terminal CLI, sub-agents, and production-ready evaluation.
#2507: The AI Design Engineer: Your New Job Title?
What happens when product thinking meets AI agents? The future of software work is here.
#2505: How Self-Hosted Search Actually Works for AI Agents
SearXNG isn't a crawler — it's a metasearch router. Here's how it works and why AI agents change everything.
#2496: Are Hidden API Endpoints Leaks or Just Plumbing?
When LLM agents discover unauthenticated JSON endpoints in browser DevTools, is it a security breach or just reading the page?
#2493: Are You Writing for Humans or AI Agents?
How GitHub repos, JSON formats, and competing standards are reshaping who (and what) you're publishing for.
#2492: When AI Agents Collapse Stack Evaluation from Weeks to Seconds
How Claude Code and agentic AI are turning GitHub into a discovery layer and collapsing library evaluation from weeks to seconds.
#2471: Creative Briefs for AI Agents: What Agencies Already Know
How agency best practices for briefing creatives map directly onto getting reliable output from AI agents like Claude Design.
#2461: How Claude Code's Conversation Compaction Actually Works
The three-tier system, what survives, what dies, and why you shouldn't rely on auto-compact.