Spent two years deploying AI agents to investigate production incidents across team boundaries. The technical part was easy. The politics nearly killed it.
Summary
The author shares a two-year experience deploying AI agents for investigating production incidents across team boundaries, highlighting that while the technical implementation was straightforward, the organizational politics posed the real challenge.
Similar Articles
why AI agent pilots feel amazing but production deployment turns into a mess
The author shares experiences moving AI agent systems from sandbox to production, highlighting how human roles become ambiguous and teams disengage when agents execute tasks, leading to operational failures.
the boring part of AI agents nobody builds and everyone needs
A practitioner recounts how deploying AI agents in production required 80% engineering effort on workflow, ownership, and approval processes rather than the model itself, highlighting that the 'boring layer' of shared context and routing is critical for real-world impact.
Has anyone deployed a multi-agent AI employee in production?
A discussion about deploying multi-agent AI systems in production, where different agents handle planning, execution, communication, and project management, asking about real-world experiences and bottlenecks.
I helped a 300-person company deploy agents. A few more lessons learned
The article shares practical lessons learned from assisting a 300-person company in deploying AI agents, highlighting challenges and takeaways for enterprise agent implementation.
Six months running an AI reviewer in the path of every production command (got surprised by what it did to the security team)
An open-source access gateway deployed an LLM-based reviewer for production commands; the unexpected effect was a transformation in the security team's role from a binary gatekeeper to a judgment layer over the AI agent.