Teams are already treating AI agents like employees. Their own email addresses. Their own credit cards. Their own authority to act. Nobody does that for software.
We’ve been building infrastructure for agents as if agents were software. They’re not.
Software does what you told it to do when you wrote it. An agent decides what to do at runtime. It picks tools, chains calls, holds credentials, and creates effects in the real world, effects that don’t roll back. When something goes wrong, it’s a repo modified, a card charged, a message sent.
We saw the shift and reused the infrastructure anyway. We took sandboxes built for deterministic code and dropped agents inside them: hard walls designed to contain a process, not govern a decision-maker. They assume the thing inside is predictable. An agent is precisely not that.
Where production agents create runtime friction
The teams building production agents figured this out before the infrastructure did. They already run agents the way they run employees: delegate authority, set limits, expect accountability. The infrastructure hasn’t caught up. Four places where it shows.
Untrusted input triggers destructive actions
A browser task, API call, or package script only needs one scoped capability.
If raw keys live in prompts, files, or environment variables, every tool becomes a trust boundary.
Compute bills climb while agents sit idle
Agent fleets sit idle between jobs or reserve memory for peaks that rarely come.
Runtime overhead creeps toward the model bill instead of staying a small line item.
Long-running jobs crash mid-flight
A network hiccup kills the container while a migration is still in progress. The memory state is gone, hours of work with it, and engineers restart from zero.
Failures leave nothing to investigate
The email went out, the record changed, the money moved. Then the run failed. Without a log of what the agent did to the world, teams cannot tell what already happened or what can be safely undone.
Constrained while they run
Software is constrained when you write it. Agents have to be constrained while they run.
That changes what the infrastructure beneath them needs to do. You can’t write all the constraints in at deploy time, because the behavior doesn’t exist yet. The constraints have to travel with the execution: scoped to the task, enforced where the agent actually reaches out, with a record left behind whether anyone’s watching or not.
The sharpest example is credentials. Software gets API keys baked in: environment variables, secrets managers, keys that exist as long as the service runs. You give agents their own credentials because you know, intuitively, that a shared credential with unbounded scope is wrong. The agent should only be able to reach what it needs, for this task, right now. That intuition is correct. It just needs a runtime that enforces it.
I started Runta because I kept seeing this gap: teams building production agents and discovering that none of the existing infrastructure was designed for the thing they were actually running. The frameworks organize the reasoning loop. The sandboxes give isolation. But no layer beneath the agent takes responsibility for what it does: the authority it exercises, the effects it creates, the record it leaves behind.
Each tool sees a slice. Teams reconstruct failures after the fact.
The evidence needed to explain a failed run: commands, packages, network calls, credentials used.
Scoped · Append-only · InvestigableRunta records execution evidence across processes, files, package installs, and network connections. Because credentials and egress are governed at the same runtime boundary, teams can investigate failures and enforce policy without handing raw secrets to the agent.
Runta is the execution layer built for agents. It sits beneath the frameworks teams already use, with a boundary around every action the agent takes. Agents act. They spend money, change code, send messages on your behalf. That kind of thing should run on infrastructure that knows it’s happening.