For years, the challenge in software security and governance hasn't been knowing what to do, but instead scaling that knowledge across fast-moving teams. At Sonatype, we invested heavily in solving that through contextual policy. Not just rules, but rules that understood intent. Rules that prioritized based on usage, risk, and relevance, and turned raw security data into actionable, in-context decisions.
Why? Developers were drowning. And without smart, contextualized guardrails, security at scale was dead on arrival. But now, we've hit an inflection point.
The rise of Agentic AI, or autonomous systems capable of coding, testing, building, and deploying software, means we are rapidly entering a world where there's no human in the loop. These agents don't file tickets, wait for approvals, or post in Slack asking, "Is this license okay?" They just go. Fast.
This is both the power and peril of agentic systems. While humans might have absorbed tribal knowledge about what’s acceptable ("we never ship GPL to customers" or "ignore CVE-2022-XXXXX if it's unreachable"), agents don't operate on lore or Slack threads. They don't benefit from mentorship or instinct. Agentic tools operate purely on what is explicitly encoded in the systems that guide them.
And that brings us back to policy.
Contextual policy, as we've built it at Sonatype, was always about more than checklists. It was about encoding real-world, real-time judgment into a system that could make the right decision, even without human intervention.
That used to mean helping a developer prioritize the one vulnerability out of 50 that actually mattered. Or flagging a license violation that was relevant in this deployment context, not just in general. It meant empowering humans with smart, risk-based guidance that fit into their tools and workflows.
But in the new model, that same capability isn't a nice-to-have. It's survival-critical.
Agentic systems operate at machine speed and scale. They can push thousands of decisions a day. You can't rely on a security team to review each one — you can't even rely on a human being present. Which means:
You need policies that understand usage context (is the vulnerable method actually called?).
You need policies that factor in business intent (is this for internal tools or external customers?).
You need policies that are dynamic and auditable, because agents must explain their decisions — to humans, to systems, to other agents.
This is the evolution of contextual policy — what used to help developers scale securely is now the bedrock for allowing autonomous systems to operate safely.
And here's the good news: Sonatype has been building toward this moment for over a decade.
We've built the engines that interpret component usage and the policy frameworks that reflect both security and licensing context. We've integrated these into every phase of the SDLC, from IDEs to CI/CD pipelines to runtime enforcement. Now, we're extending that foundation to enable the next generation of software systems: the autonomous agents that will build tomorrow's applications.
These agents don't need dashboards. They need guardrails, ground truth, and governance encoded as code. That's exactly what contextual policy delivers and why it's indispensable in the era of agentic development.
There's no turning back. Whether you call it DevOps++ or AgentOps, the age of autonomous software builders is here, and the role of contextual policy has changed. It's not just about helping humans make better decisions, but also about ensuring machines don’t make catastrophic ones.
Humans needed policy to scale; machines need it to survive — and Sonatype's ready.