Building Governable, Connected AI Agent Systems
Reliable AI agents require durable skills, managed execution environments, measurable workflows, agent-to-agent infrastructure, and continuous security testing.
Summary
AI agents become more effective when successful procedures persist as governed skills, run inside managed sandboxes, and improve from failed traces rather than relying on model changes alone. Scaling them requires end-to-end software workflows, blended roles, platform metrics, and software factories that connect planning, coding, testing, deployment, and monitoring. Agent collaboration needs registries, persistence, identity, queues, observability, and ordered communication beyond the stateless patterns of MCP, A2A, and human messaging platforms. Costs vary sharply by model and subscription, while insecure generated code and attacks arriving within minutes make continuous offensive testing essential.