Governing the Agentic SDLC Through Continuous Policy Enforcement

AI agents increasingly perform software development and release tasks, but conventional governance systems such as Git logs and human-centered compliance controls generally capture only final changes rather than the models, prompts, tools, decisions, and accountability behind agent actions. The passage argues that this gap creates risks including ineffective human review at scale, non-deterministic behavior, vulnerable dependencies, prompt injection, and malicious tool integrations, while emerging requirements such as the EU AI Act and OWASP AISVS emphasize oversight, logging, and auditability. JFrog proposes Agentic Process Evidence, an open, SLSA-compatible protocol that records agent session telemetry, cryptographically signs provenance attestations, links them to commits, artifacts, or releases, and evaluates them through policy-as-code gates. The protocol distinguishes individual agent sessions from broader processes and captures agent and model identities, tool usage, contextual inputs, ownership, reviews, execution logs, and compliance outcomes, enabling organizations to trace affected releases, enforce approved models and tools, target human review toward higher-risk work, and support audits or incident investigations. JFrog’s AppTrust Prompt to Release Traceability is presented as an implementation of this approach, while the company invites standards bodies, AI providers, and SDLC platform vendors to collaborate on an interoperable evidence standard rather than separate proprietary formats.

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