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Guardrails for GenAI: Mitigating risk in autonomous software pipelines

By Harry Rao, Founder and CEO of TestGrid

You ask an AI agent to resolve a bug that is delaying a software release. It reads the ticket, examines the relevant code, decides which tools to use, updates the affected files, runs the test suite, and responds to any failures it encounters.

Within minutes, it opens a pull request that appears complete. Yet if the agent has broader access than the task requires, a malicious instruction or plausible coding error can move through the pipeline before a reviewer sees the full chain of actions.

Stack Overflow’s 2025 Developer Survey found that 84% of respondents were using or planning to use AI tools in their development process.

Now, as these tools gain more freedom to make autonomous decisions, your existing security delivery controls need closer scrutiny. You need guardrails at points where agent actions might affect production outcomes: access, approval, and release....

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A proof of concept forgives a fragile data path. Operational AI does not.

Presented by F5 When enterprises move AI workloads from pilot to production, data delivery often becomes the factor that determines whether those systems can scale reliably. Point-to-point architectures connecting storage directly to compute hold up under demonstration conditions, but they often break down under sustained, concurrent production traffic. The result

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A proof of concept forgives a fragile data path. Operational AI does not.