Unlock Agent Autonomy: The Runtime for AI-Native Systems

Official Schedule Context

The way software gets built in 2026 doesn't look like it did in 2024. The actors changed. Agents

read and write entire codebases. Subagents spawn to chase down a flaky test, refactor a module, or

triage an incident. But this shift doesn't stop at the SDLC. Agents increasingly invoke tools,

interact with enterprise systems, install dependencies, call APIs, and orchestrate workflows across

local machines, CI systems, cloud infrastructure, and organizational boundaries. The teams leaning

into this shift are moving faster, and the gap is widening by the quarter. But few have the

confidence to let agents operate autonomously across those environments. Not because the model

capability isn't there. Trust isn't. Agents can pull a poisoned dependency, invoke an untrusted

tool, wipe a database, leak sensitive data, or access systems they shouldn’t. Prompt-level

instructions won't close that gap, the unlock has to happen one layer down, at the runtime layer

itself. Docker spent the last decade making it safe to ship software by getting the runtime

right: isolation, network policy, trusted base images, and credentials. Agents are the next

workload, and the same principles apply. Tushar Jain, EVP of Engineering at Docker, walks through

what the runtime layer for AI-native systems looks like in practice: hardened runtime foundations,

sandboxes that constrain what agents can touch, and governance controls that limit what agents can

introduce, access, and execute across local, CI, cloud, and enterprise environments. The pattern is

the same on every vector: reduce the surface area of what the agent gets to decide, so the parts

that matter aren't left to a prompt. Attendees leave with a clearer framework for giving agents

more autonomy safely. Engineers see how agentic applications can operate across tools and

infrastructure. Security leaders get a runtime model that maps to controls they already understand.

Platform teams get a way to scale agent execution without standing up a new runtime for every team.

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