Faramesh-Core from Faramesh provides a governance engine that enforces policy controls over AI agent tool calls for security and compliance. It intercepts and evaluates tool invocations using deterministic, code-defined rules, brokers credentials, and records decision provenance to make actions auditable. The key design emphasizes policy-as-code and versionable governance rules, with multiple interception tiers for deployment flexibility. Targeted at software developers, AI engineers, and enterprise security teams, it helps formalize agent tool access and audit trails.

What tasks can you actually use the tool for?

The tool performs runtime control of agent interactions with external systems by acting as a policy-enforcement layer. Use cases include: authorizing or blocking tool calls, mediating credential use, and producing audit records for compliance reviews. Typical tasks map to deployment stages where agents call APIs or SDKs. Practical outcomes are controlled execution of agent actions and machine-verifiable decision logs that support review workflows.

How deterministic and auditable are governance decisions?

Decisions are made by deterministic rules that the developer describes as code, so the outcome depends on policy definitions rather than heuristic scoring. The engine stores detailed provenance for each decision, which supports forensic review and regulatory evidence requirements. Audit fidelity therefore aligns with the granularity of rules and the completeness of recorded metadata, making traceability strong when policies include decision metadata.

Does it require technical setup or fit existing workflows?

The engine is optimized for the MCP platform and integrates with common AI SDKs and cloud identity systems, so it fits projects already using that ecosystem. Deployment options include an SDK shim, an MCP proxy, and an HTTP proxy, allowing teams to choose a level of insertion that matches their architecture. Adopting it requires writing and maintaining policy code and mapping identity principals to access rules.

Suitable for teams that need auditable, rule-driven control over agent actions

The tool is a practical option for engineering and security teams that require deterministic gatekeeping and verifiable decision logs, because it enforces code-defined rules and produces provenance records. Teams should treat policy development as a software artifact, keep rules in version control, and include policy tests in CI pipelines to avoid authorization gaps during rollout.