AI Agents Need Platform Engineering, Too

The first generation of AI coding assistants helped developers write software faster, but the next generation of autonomous AI agents are now beginning to provision infrastructure, deploy applications, troubleshoot systems and execute operational tasks with minimal human intervention.

As that shift accelerates, platform engineering is taking on a new role: providing the governance, security and self-service capabilities that allow AI agents to operate safely alongside human developers.

The challenge is in giving AI agents enough autonomy to be productive without sacrificing visibility, accountability or control.

Building Platforms for Machine Consumers

Many organizations still think of AI agents as another form of automation. That’s the wrong approach, says Yasmin Rajabi, chief operating officer at CloudBolt.

“Treat agents as first-class users, not traditional automation,” she says. “They need the same golden paths, role-based access control (RBAC), and audit trail as developers, just exposed through machine interfaces like MCP, not a user interface.”

The same principles platform engineering teams have spent years building for developers—self-service infrastructure, standardized workflows and governed access—must now be extended to machine consumers. Otherwise, AI agents may begin finding their own ways around established processes.

“Prioritize giving agents access to the data they need in a way that is governed so the agent doesn’t look for an alternative path,” Rajabi says.

She adds that in the same way you wouldn’t want humans to develop workarounds, know that intelligent agent workflows will as well.

“Give them the ideal path and make it first class,” she explains. 

Governance Becomes Non-Negotiable

As AI agents move beyond code generation into provisioning infrastructure and deploying software, governance becomes the foundation for safe autonomy.

“Once an agent can provision or deploy, three controls stop being optional,” Rajabi says. “It runs under platform-defined auth and inherits the required RBAC and nothing more; every action is audited, and its spend is scoped, since an agent stuck in a loop burns budget as fast as compute.”

Gary Sidhu, senior vice president of product engineering at GTT, says the bigger issue is ensuring AI remains accountable to human decision-makers.

“AI does not inherently understand business context or intent; it acts on the objective and information it is given,” Sidhu says. “That makes human accountability easy to preach but hard to operationalize.”

As agents take on more operational responsibility, organizations need governance models that clearly define ownership before something goes wrong.

“As AI agents take on more responsibility across workflows, organizations need controls that make it clear who owns the decision, who owns the action, and where accountability sits when something goes wrong,” Sidhu says. “AI should operate within human-defined boundaries, serving as the execution layer rather than the source of business judgment.”

Without those guardrails, organizations risk policy drift, where AI systems gradually operate beyond their intended purpose, as well as conflicts between multiple agents pursuing competing objectives.

Autonomy Requires Guardrails

The goal isn’t to keep humans involved in every operational task. It’s to establish clear policies that allow AI agents to execute confidently within defined limits.

“Autonomy isn’t the absence of supervision; you grant it by scoping,” Rajabi says. “An agent gets the same identity, permissions, and audit as the human it acts for, plus budget caps and runaway detection. Start read-only with a human in the loop and widen the blast radius as the guardrails prove out.”

Sidhu says he expects software delivery itself to become increasingly policy-driven, with humans defining objectives while AI agents execute the work.

“They’ll become less about humans manually moving work through each stage and more about humans defining the intent and approval thresholds that AI agents execute against,” Sidhu says. 

As agents take on more operational work, workflows will need clearer oversight and stronger policy consistency.

That consistency becomes even more important as multiple autonomous agents begin operating simultaneously.

“Without a clear governance structure, multiple AI agents operating simultaneously in the same environment may pursue conflicting objectives or respond to one another in unintended ways, ultimately producing outcomes that diverge from the original goal,” Sidhu says.