Guardrails for AI Agents: How to Keep Autonomous Systems from Becoming an SRE Nightmare

Autonomous AI agents are quickly moving from research demos into production environments. For site reliability engineers (SREs), infrastructure leaders, and engineering managers, that shift raises a hard question: how do you unlock the upside of agents that can act on your systems without turning operations into a 24/7 incident drill?

The answer, according to guidance from PagerDuty executive João Freitas, is not to slow down adoption, but to surround AI agents with strong guardrails. That means rigorous governance, security, and observability so autonomous behavior never becomes a black box that threatens reliability.

This explainer breaks down where AI agents create operational risk and how SRE and ops teams can design guardrails that keep control of production systems while still benefiting from autonomy.

The rise of AI agents in production

Large organizations are already deep into AI experimentation and deployment, and many are now moving from passive assistants (like chatbots) to active, goal-driven agents. These agents don’t just suggest actions; they can be granted the ability to take actions directly on systems.

More than half of organizations have already deployed AI agents to some degree, and many more expect to follow suit in the next two years. That rapid adoption has exposed a gap: a significant share of tech leaders now say they regret not putting a stronger governance foundation in place from the start.

That regret is telling. It suggests AI agents have often been introduced quickly to capture perceived ROI, while policies, rules, and operational best practices have lagged. For SRE teams, this is familiar territory: any new automation that can touch production must be treated as a potential source of outages unless it is wrapped in the same discipline applied to human operators and traditional automation.

As AI adoption accelerates, the key challenge is balancing speed and experimentation with exposure risk. That balance comes from explicit guardrails, not from blocking the technology altogether.

Where AI agents create operational risk for SRE teams

From an SRE perspective, the risk profile of AI agents clusters around three areas: shadow usage, unclear accountability, and lack of explainability.

1. Shadow AI acting on systems you don’t control

Shadow AI occurs when employees use unsanctioned or unauthorized AI tools without approval from IT or security. This problem is not new, but autonomous agents amplify it. An engineer experimenting with a hosted agent that can call APIs or access internal tools might bypass standard processes entirely, introducing fresh security and reliability risks.

Without visibility into which agents exist, what credentials they hold, and what systems they can touch, SRE and ops teams are left blind to a new class of changes that can impact production.

2. Gaps in ownership and accountability

The power of AI agents is their autonomy: they can take steps toward a goal based on context and policies. But autonomy creates a critical question for incident response: when something goes wrong, who owns the response?

If an AI agent triggers an unexpected workflow, misconfigures a service, or causes performance regressions, teams need a clear owner for that agent. Without that accountability, incidents can stall while teams debate whether the issue belongs to SRE, security, or the application team.

3. Non-explainable actions and black-box behavior

AI agents are goal-oriented. They are designed to figure out how to accomplish objectives, sometimes through sequences of complex steps. The danger comes when those steps are opaque to the humans responsible for reliability.

If engineers cannot see how the agent arrived at a decision or what intermediate actions it took, it becomes difficult to trace issues, roll back changes, or confidently trust the agent in more critical workflows. Lack of explainability directly undermines both incident response and long-term reliability engineering.

None of these risks are arguments against AI agents. Instead, they define the problem space SRE and infrastructure teams must solve if they want AI to accelerate operations instead of destabilizing them.

Principle 1: Make human oversight the default

For any agent that can act on key systems, human oversight should be the default posture, especially in early stages of deployment.

Keep humans “in the loop” for business-critical systems

As agent capabilities evolve quickly, production teams cannot assume that an agent’s decisions are always safe or aligned with operational constraints. For business-critical use cases and core systems, humans should review and approve impactful actions by default.

That means designing workflows where the AI agent can propose actions, but a human operator validates them before execution—at least until there is sufficient confidence and operational experience to relax those controls.

Assign clear human owners for each agent

Every agent should have a designated human owner responsible for oversight and accountability. This is not a theoretical governance task; it is a concrete operational requirement so that when behavior looks suspicious, someone knows they are on the hook to investigate and remediate.

Operations, engineering, and security teams should also understand their respective roles in supervising agent workflows. Beyond the primary owner, any human who observes a negative outcome should have the ability to flag, pause, or override an AI agent’s behavior.

Start conservatively and control the action surface

Traditional automation works best for repeatable, rule-based processes with predictable inputs. AI agents, by contrast, are attractive because they can tackle more complex tasks and adapt to new information. That flexibility is also what makes them risky.

As agents are deployed, SRE and infra teams should explicitly limit what actions those agents can take, particularly early on. High-impact actions—such as configuration changes, production deployments, or failover operations—should flow through defined approval paths governed by humans.

Controlling the action surface and gradually expanding autonomy as confidence grows allows teams to benefit from agents without handing them unlimited operational power from day one.

Principle 2: Bake in security and access control

Security and reliability are tightly coupled. New tools that operate without guardrails do not just create security issues; they create reliability incidents. For AI agents, security must be treated as a first-class concern in the design of your agent platform.

Use platforms that meet enterprise security standards

Organizations should favor agentic platforms that comply with high security standards and have enterprise-grade certifications such as SOC 2, FedRAMP, or equivalent. While certifications alone do not guarantee safety, they indicate that basic security controls and processes have been scrutinized.

Align agent permissions with their human owners

AI agents should never be granted broader permissions than the humans responsible for them. At minimum, the security scope and access level of each agent should match the scope of its owner.

Additionally, any tools or integrations added to an agent must not silently extend its permissions. Without that discipline, attaching a new integration could accidentally grant the agent cross-system powers that no single human would have in practice.

Apply least privilege and role-based access

Limiting agent access based on role and specific responsibilities helps ensure smoother deployment and reduces blast radius during failures. For SREs, this mirrors existing principles for service accounts and automation scripts: agents should be treated similarly.

Log everything for incident response

Complete, reliable logs of every action taken by an AI agent are critical. In the event of an incident, engineers need to understand precisely what the agent did, in what order, and with what inputs and outputs.

Comprehensive logging turns agents from opaque actors into traceable systems that can be audited, debugged, and improved over time.

Principle 3: Make agent outputs explainable

AI use in operations cannot be a black box. For SREs, the ability to understand why a system behaved a certain way is core to both incident management and long-term reliability work. The same must apply to AI agents.

Ensure the reasoning behind actions is accessible

For every meaningful action an AI agent takes, the reasoning and context should be visible to engineers. That includes what inputs the agent saw, what intermediate steps it took, and how it arrived at the final decision.

Making this reasoning accessible allows any engineer to reconstruct the agent’s thought process. It also supports post-incident reviews where teams examine not just what went wrong technically, but whether the agent’s decision-making should be adjusted.

Log inputs and outputs for every step

Inputs and outputs for every action should be logged and easy to query. Over time, this creates a rich trace of agent behavior that can be used both to diagnose specific issues and to understand broader patterns in how agents interact with systems.

These traces are particularly valuable when something goes wrong: they provide a concrete trail that SREs can follow to identify the root cause, determine whether the agent exceeded its intended scope, and decide whether guardrails need to be tightened.

What this means for SRE and infrastructure teams

For SREs, AI agents represent both an opportunity and a new operational surface area. They can accelerate toil reduction, help manage complex workflows, and augment incident response. But without guardrails, they also introduce unpredictable change into the very systems SREs are charged with stabilizing.

Practically, this guidance suggests several priorities:

• Work with leadership to build a governance foundation for agents before they are widely deployed.
• Map where agents can touch production systems and ensure those paths have human oversight by default.
• Treat AI agents like powerful automation: apply least privilege, strict access control, and comprehensive logging.
• Insist on explainability and traceability as non-negotiable requirements for any agent platform or integration.

The goal is not to resist AI agents, but to integrate them into existing reliability practices so they become reliable collaborators rather than sources of surprise.

Security and governance as the backbone of successful AI agents

AI agents offer significant upside for organizations looking to accelerate and improve existing processes. But that upside is conditional. Without strong security and governance, the same autonomy that makes agents powerful can expose organizations to new types of incidents and reliability risks.

As AI agents become more common in production, organizations need systems to measure their performance, detect when they misbehave, and respond quickly when they create problems. For SREs and infrastructure leaders, that means treating AI agents as first-class components of the reliability ecosystem—subject to the same discipline, observability, and guardrails as any other critical system.

Handled this way, autonomous agents do not have to be an SRE nightmare. With the right guardrails, they can become another well-governed tool in the reliability toolkit.