Your agent and your team should have the same source of truth, but most setups don't
Summary
Highlights the common disconnect between AI agents and human teams sharing the same source of truth, and how most current setups fail to achieve this.
Similar Articles
AI agents fail in ways nobody writes about. Here's what I've actually seen.
The article highlights practical system-level failures in AI agent workflows, such as context bleed and hallucinated details, arguing that these are often infrastructure issues rather than model defects.
Agents are meant to be shared, but existing tooling is not fit for purpose
The author discusses the difficulty of sharing AI agent workflows across teams and introduces Nairi, a tool for deploying Claude Code-backed agents in Slack with shared access.
Where AI agents actually break in real workflows (not demos)
A discussion on where AI agents fail in real workflows, highlighting issues with coordination, reliability under messy inputs, and the challenge of reducing human intervention in production.
everyone's focused on whether their agent works. almost nobody asks if it's actually getting better over time
The article points out a common oversight in AI agent development: while most teams monitor task completion, few systems capture and feed failure patterns back into future runs to enable learning and improvement over time.
After building agent teams for a dozen clients, here's what actually made them trust the system (and stop babysitting it)
The author shares practical insights on building client trust in AI agent systems, emphasizing the importance of narrow scope, robust error handling, and clear communication of system status.