Introducing the Paragon Agent Sandbox

Why agent QA is a different problem

Most teams validate agents with evals inherited from model testing. Evals score outputs against a reference set. That works for comparing models. It does not catch what deployed agents get wrong.

Three patterns expose the gap.

Agents make sequences of tool calls, not single completions. A correct final output can come from a path that was three extra calls long and three times as expensive. The eval passes. The agent is burning tokens on the wrong route, and the cost, latency, and failure surface compound in production.

Agents drift as models update, prompts change, and traffic shifts. Uptime Robot's 2026 monitoring guide breaks drift into semantic, response-distribution, and retrieval drift. None of them fire as a single test failure. Quality slips slowly.

Agents escape their declared boundaries under combinations their authors did not anticipate. Eval suites test happy paths. Boundary escape lives in the corners nobody wrote a test for.

Atlan's six-layer agent testing framework puts agent project failure in production at eighty to ninety percent, with missing validation infrastructure as the dominant cause.

What the sandbox does

Agents run inside Paragon's sandbox in an isolated environment that mirrors production. Every tool call, browser action, and workflow step is recorded, scored, and compared to prior successful runs.

Tool-call validation. Each tool call the agent attempts passes through a checker that verifies the expected schema, the authorization scope, and whether the call actually fits the goal. Paragon answers three questions per call: did the agent pick the right tool, pass the right arguments, and produce an output that satisfied the goal. Composio's 2026 guide frames this as the core agent reliability problem. The sandbox has validated over 3,500 tool calls across pilot teams.

Web interaction replay. Agents that browse or operate on live sites run inside an instrumented browser. Every action is recorded and compared to a known-good sequence. When the agent clicks a different element, skips a step, or loops unexpectedly, the divergence is flagged at the exact step.

Autonomous workflow execution. Multi-step workflows replay end to end with injected edge cases, faults, and adversarial inputs. The sandbox scores the final behavior against the declared objective, not just the final string.

Regression detection. When a new agent version behaves differently on inputs that previously succeeded, the regression is flagged before deploy. No more discovering it from a user complaint.

Boundary and policy validation. Teams declare what the agent is allowed to do. The sandbox verifies on every run. Microsoft's 2026 Foundry guidance covers structured tool-invocation schemas and just-in-time authorization as the pattern. Paragon runs the same pattern at tool-call time.

Production-trace replay. Real production traces replay inside the sandbox when a new agent version is proposed. Regressions get caught pre-deploy instead of pre-rollback.

How the sandbox is built

Billing is per-second of runtime plus resources consumed, matching the pricing conventions of E2B, Daytona, and Modal. Teams pay for what they validate.

Each agent runs in its own isolated microVM so it can execute code, call tools, and drive a browser without affecting anything else. Northflank's 2026 sandbox research covers why microVM isolation (Firecracker, Kata) is the right default for untrusted agent workloads.

Install is GitHub-native. Teams install once, declare their agent, and send traffic into the sandbox through the same login they already use.

Compliance is SOC 2.

Where it fits in the validation stack

The sandbox is complementary to evals, sandbox compute, and observability, not a replacement.

• LLM eval platforms (Braintrust, Galileo, Maxim) score the agent's answers. Paragon scores what the agent actually did: which tools it called, in what order, and whether it stayed within its allowed actions. Run both.
• Sandbox compute providers (E2B, Daytona, Northflank) give agents an isolated place to run code. Paragon runs a sandbox too, but adds the full set of checks that tell you whether the agent behaved correctly. Different layers.
• AI observability (Arize, Fiddler) watches agents after they are deployed. Paragon catches issues before deploy. Different sides of the same problem.

Paragon is the only product today that validates an agent's full behavior before it ships. Evals score answers. Compute providers run code. Observability watches what happens after. Paragon covers the space in between, which is what teams need to ship agents with confidence.

What we have seen so far

Five hundred plus sandbox sessions across pilot teams. Three thousand five hundred plus tool calls validated. Teams report catching regressions pre-deploy that had previously surfaced as user complaints days later. The adoption pattern has been consistent: teams plug in their agent, receive specific proofs on what was wrong, and shift their QA attention from PRs to agents.

That pattern is what the sandbox was built for.