The hard problems weren’t model choice or prompt quality. They showed up after deployment.

Agents started calling tools nobody remembered wiring up. LLMs accessed internal APIs through chains that weren’t obvious from logs. Execution identity diverged from user identity. MCP servers became quiet but critical control planes.

Most existing security assumptions break in these scenarios because they assume:

static services

clear ownership

single-hop execution

pre-defined boundaries

AI systems violate all of those.

We recently spent a week documenting and shipping solutions around runtime visibility and governance for AI systems, focusing on how agents, MCP servers, APIs, and models actually behave once live.

Instead of high-level frameworks, we tried to answer practical questions:

What exists right now?

Who is acting on whose behalf?

What tools are being invoked, and in what sequence?

Where does data flow during real executions?

We wrote up the learnings and what we built here: https://www.levo.ai/resources/product-release/launch-week-2026-recap

Not posting this as a “launch”, more as a discussion starter. Curious how others are thinking about securing AI systems once they stop being demos and start being infrastructure.