Agents can do the work

Summary

Agent evaluations often ask what an AI Agent can do, but the larger constraint may be whether an organization is structured to let it act. Across organizations in energy, healthcare, ecommerce, gaming, financial services, and property management, teams found that Agents could often perform actions technically, but stalled on unreliable identification, inaccessible live data, or unreachable vendor systems. The post describes five readiness types—content, scope, procedural, data, and execution—and says deeper automation depends on documented processes, trustworthy connected data, stable APIs, and clear ownership of risk. Teams that lacked these foundations often redesigned toward guiding users rather than executing changes, while teams with standardized operations could trigger tightly controlled actions. The unresolved question is how readiness develops, since the gaps may involve undocumented procedures, fragmented systems, missing APIs, or infrastructure and accountability investments beyond buying a more capable Agent.

Takeaways

1. 1

   Readiness has five dimensions: content, scope, procedural, data, and execution. Most organizations have the first two, while fewer have reliable processes, connected data, stable systems, and accepted ownership of execution risk.

2. 2

   Teams attempting application changes, billing automation, or third-party status checks often encountered unreliable identity resolution, regulatory limits on live data, or inaccessible vendor systems, then shifted toward guiding users through processes.

3. 3

   Deeper automation was associated with standardized operations: actions were already well-defined, consistently applied, and exposed through stable systems with clear rules, rather than requiring the Agent to invent new behavior.