The agent setups still running three weeks later have five things in common
Summary
The article outlines five key practices that differentiate successful long-running AI agent setups, emphasizing orchestration, chartering, proportional access, correction storage, and reversibility-based approval lines.
Similar Articles
The hard part of agents is not building one. It is operating five.
The article discusses the operational challenges of running multiple AI agents in production, emphasizing observability, recovery, and session management over the initial development of a single agent.
Before launching an AI agent, I think these things are worth considering
The article discusses key considerations before launching an AI agent, emphasizing action capability, context access, escalation, handoff, and success measurement.
The Real Truth About AI Agents
An experienced practitioner shares hard-won lessons from deploying 25+ AI agents to production, arguing that memory, orchestration, and auditability matter far more than model choice. The article details common failure modes like context loss and silent cost loops, and recommends a stack including Claude Sonnet 4, Pydantic AI, and dedicated memory layers like Octopodas.
Lessons from months of running a mixed fleet of coding agents on the same repos
The article shares lessons learned from using multiple AI coding agents on the same code repositories over several months, covering insights on their effectiveness and challenges.
Your processes are supposed to get better. Almost none of them do. Here's what we learned trying to close the loop.
After 8 months of deploying AI agents on real operational tasks, the author shares five unexpected engineering challenges: per-capability permissions, credential isolation via a connector proxy, durable approval gates, hard budget caps, and out-of-process audit logs.