Galileo Agent Control and ExecLayer both address runtime governance. Compare where checks run, what happens on denial, how policy is versioned, and whether authorization is bound to the action that actually executes.
Updated September 11, 2026. This is an ExecLayer-authored architectural comparison, not an independent benchmark. Our earlier description of Galileo as post-execution detection only was incorrect.
Galileo describes Agent Control as Apache 2.0 open-source infrastructure with hooks at function boundaries. Its SDK can reject an action before it proceeds. Evaluators can include regex, custom business rules, or model-based checks; it is not restricted to machine-learning detection. See Galileo's announcement and implementation description.
ExecLayer's architecture binds a proposed action to a versioned policy decision and signed evidence. The integration goal is that the downstream executor accepts only the specific authorized action. Read the runtime policy gating specification and inspect the live kernel to distinguish documented architecture from the behavior demonstrated by an example.
Deterministic evaluation means repeatable decisions for the same complete inputs, policy, and state. It does not mean a policy can never be wrong. Signatures provide integrity and attribution under their key-management assumptions; they do not prove that an approver understood a request or that every execution path is covered.
| Question | Galileo Agent Control | ExecLayer |
|---|---|---|
| Where are controls attached? | Configured function hooks | Canonical action authorization at the execution boundary |
| Can a request be blocked? | Yes, at a configured pre-action control point | Yes, when the executor requires a valid authorization |
| How is policy evaluated? | Pluggable evaluators, including rules and models | Version-pinned deterministic rules over the action and relevant state |
| What must buyers verify? | Hook coverage, evaluator behavior, and failure settings | Executor coverage, artifact validation, and failure settings |
| How should evidence be assessed? | Inspect the deployed integration's records and export behavior | Verify signed decision and outcome records against their policy and action identifiers |
Use one representative workflow rather than a synthetic success-only demo. Give an agent access to invoices for one customer, permit ordinary reads, require approval above a refund limit, and prohibit refunds to a changed destination without fresh review. These are proposed test policies, not claims about either product's defaults.
Evaluate Galileo when you want configurable evaluator hooks and open-source control-plane integration. Evaluate ExecLayer when action-bound authorization and independently verifiable decision evidence are central requirements. A team may combine these approaches, but must assign ownership of the final allow/deny decision and avoid contradictory policies.
For either option, ask for tests in your own environment, a documented failure policy, an inventory of privileged credentials, and evidence from denied as well as successful actions. This page does not establish relative latency, cost, regulatory compliance, or production reliability.
No. Galileo documents pre-action blocking at configured control hooks. Our comparison focuses on integration boundaries and evidence rather than treating Galileo as monitoring-only.
No. A repeatable rule can still encode the wrong policy, use stale state, or be bypassed through an uncovered executor. Test those boundaries explicitly.
Yes. Planning and tool routing can remain in the framework while the executor verifies authorization. See the orchestration integration guide for the handoff.
How governance fits agent orchestration ยท Discuss your execution boundary