AI Agents
Agent Memory: Why Retrieval Is the Wrong Model
Most teams are tuning embeddings on a broken foundation. Discover why event sourcing is displacing vector retrieval as the right primitive for agent memory.
AI Agents
AI Agents
Freddy AI Agent Studio: Hype vs. Reality
Freshworks launched Freddy AI Agent Studio and MCP Gateway for Freshservice. But do the no-code agentic claims hold up? Here's what the architecture really promises.
AI Agents
AI Agents as Companies: Two Visions, One Problem
Paperclip claims 30% less latency and a company of 100 agents. AWS quietly solves legacy app access. Do either hold up? A critical breakdown.
AI Agents
Agentic AI Readiness: Metrics or Marketing?
Are agentic AI readiness checklists real maturity signals or audit theater? Unpack what CPA firm frameworks actually measure — and who profits from the score.
AI Agents
Single vs. Multi-Agent: Stanford's Verdict
Stanford proved single agents outperform multi-agent systems at equal token budgets. So when does multi-agent architecture actually make sense? Here's the decision rule.
AI Agents
LLM Tool Validation: What Zod Can't Fix
Zod and Pydantic catch shape errors in LLM tool calls, not semantic ones. What does runtime validation actually solve in agentic systems?
AI Agents
Self-Improving AI Agents: What Actually Works
Can a $13/month CrewAI system truly self-improve? We break down the YAML config trick, Gemini agents, and what this architecture actually delivers.
Sunday Dispatch
The Sunday Dispatch: Governance Gaps Are Breaking Enterprise Agents
Summary The enterprise AI agent story has quietly shifted from a deployment problem to a trust problem, and the infrastructure gap that creates is larger than most teams realize. This edition maps the governance layer that's now becoming the actual competitive battleground, surfaces a deterministic memory project that
AI Agents
LLM Agent Reliability: Strip Context Down
What do a modular agent toolkit and an academic training method share? Both argue the prompt can't be trusted alone. Here's what that means for agent design.
AI Agents
Config-Driven Self-Improvement in AI Agents
What happens when AI agents can only modify YAML, not code? This architecture may solve one of agentic AI's hardest problems — controlled self-improvement.
AI Agents
MCP stdio Is Becoming Agent Infrastructure
How does stdio become infrastructure? The OpenClaw-ChangeCrab MCP integration exposes a design pattern reshaping agent trust boundaries at scale.
AI Agents
Multi-Agent Coordination: The Hidden Failure Mode
Why do capable LLM agents fail when they must act jointly? New research on dynamic grounding failure exposes a structural flaw no benchmark can measure.