What Is Agentic Infrastructure? Two Meanings Explained
TL;DR
- Agentic infrastructure means either the systems that run AI agents or IT infrastructure that agents operate through governed interfaces.
- Augment Code describes the first meaning, while Qovery uses the second. These are examples of vendor usage, not a settled technical standard.
- This article focuses on the systems that support AI agents during production incident investigation and response.
- An execution harness directs the investigation. Tool connections provide access, while operational context and memory preserve relevant findings.
- Human approval and governance constrain actions, and auditable records show what the agent did.
What is agentic infrastructure?
Agentic infrastructure refers to either the systems needed to run AI agents or IT infrastructure that agents can operate under defined controls.
Vendors use the term in both ways. Augment Code describes a stack for running agents, including orchestration, memory, and security. Qovery describes cloud infrastructure that agents operate through a governed interface. Pulumi discusses agents that propose changes to infrastructure code for human review. These examples show current vendor usage, not a settled technical standard.
Two meanings of agentic infrastructure
Infrastructure for agents provides the services an AI agent needs to run. Augment Code uses the term for a stack that includes compute, orchestration, context, observability, and security. For example, an agent investigating an incident needs to keep track of its work across tool calls and retain evidence as the investigation develops.
Infrastructure operated by agents refers instead to IT systems that agents can access through controlled interfaces. Qovery describes a governed API through which agents can operate infrastructure. Pulumi describes a related approach in which its agent previews infrastructure changes, checks policy, and opens pull requests for review. Both usages describe real needs, but the vendors apply the same term to different parts of an agent’s work.
The rest of this article focuses on infrastructure for agents in production SRE work. An SRE agent needs to investigate an incident across multiple tools, retain operational context, and work within approval boundaries. Controlled access to IT systems supports that work, but an infrastructure API alone does not run or supervise the investigation.
Agentic infrastructure for production SRE
In production SRE, agentic infrastructure supports an AI agent as it investigates incidents and responds under human supervision. This applies the broader meaning of infrastructure for running agents to operational work. It does not describe a new category or IT infrastructure redesigned for agents to operate.
A production investigation needs an execution harness to coordinate work and tool connections to retrieve evidence. Operational context and memory help the agent retain findings across steps. Approval controls govern proposed production changes, while audit records let an engineer inspect the agent’s calls. In the payment-failure example below, AURA investigates a deployment and recommends a rollback; the documented run does not execute it.
How the components work in a production investigation
Mezmo’s Apache 2.0 open-source AURA provides a worked example of an SRE agent and execution harness investigating a production incident. In its documented payment-failure investigation, three specialist agents correlate Mezmo traces and logs with Prometheus latency and Kubernetes deployment history. They identify an N+1 regression introduced by productcatalogservice 1.13.2 and recommend rolling back to 1.13.1. AURA does not execute the rollback in the documented investigation.
The execution harness controls which specialist can do what. AURA defines workers in TOML, with separate prompts and tool lists for each role. A worker can inspect a cluster without receiving a tool for changing it; permissions in the connected system also limit what that tool can do. The AURA documentation describes how the harness restricts tool access, handles failed calls, and preserves state across turns.
Tool connections let the workers gather evidence without giving every worker the same access. AURA connects to compatible tools through the Model Context Protocol, or MCP. For this incident, access to telemetry and deployment history lets the workers compare the rise in payment failures with the service version that changed.
Operational context helps the workers interpret that evidence. AURA can retrieve information from connected knowledge bases, while its Scratchpad manages context during a longer investigation. A connected runbook could provide guidance for assessing a proposed rollback, although the documented payment-failure investigation does not show one being used.
A recommendation and a production change follow different paths. If an operator configures a rollback tool for AURA, the human approval gate can pause that write for a decision while read-only investigation proceeds. AURA does not perform the rollback in the documented example. Throughout a run, AURA emits OpenTelemetry spans for model and tool calls, giving you a record to inspect after the investigation.
Production requirements, examples, and boundaries
In AURA’s documented payment-failure investigation, the agent recommends a rollback but does not perform one. The table separates what each production requirement contributes from what it cannot guarantee.
Where governance fits and where it doesn't
Mezmo Governance works with any agent. It is not open source and does not require AURA. Governance sets boundaries for what an agent may read, propose, or execute. In the payment incident, a policy could require human approval before an agent changes production configuration, regardless of which agent proposes the change.
AURA occupies a different layer. It is Mezmo’s Apache 2.0 open-source SRE agent and execution harness for investigating incidents and taking supervised actions. AURA can pause its own work for human approval, but that approval gate does not make AURA the agent-agnostic governance layer.
Why this distinction matters for buyers
When a provider uses “agentic infrastructure,” ask whether its product runs AI agents or lets agents operate IT systems. The first use calls for questions about execution, tool access, and investigation records. The second calls for questions about which production changes an agent can make and who approves them. A product may address both, but the label alone does not tell you which capabilities it provides.
For production SRE, evaluate the execution harness separately from the controls around it. Ask a provider or open-source project to show where an investigation runs, how an action gets approval, and what you can inspect afterward. AURA offers an Apache 2.0 open-source SRE agent and execution harness you can inspect; Mezmo Governance is a separate, agent-agnostic product. Mezmo’s AI SRE explainer covers the broader use case.
FAQs
Is agentic infrastructure the same as AIOps or MLOps? No. AIOps applies AI to IT operations, and MLOps manages the model lifecycle. Agentic infrastructure refers either to the stack that runs agents or to IT infrastructure that agents can operate.
Does agentic infrastructure require open source? No. You can use open-source or proprietary components. Mezmo’s AURA repository provides an Apache 2.0 open-source example of an SRE agent and execution harness.
Is AURA a full infrastructure stack? No. AURA runs production investigations using connected tools, models, and operational data. You still provide and manage those surrounding systems.
What is the difference between AURA and Mezmo Governance? AURA is an open-source SRE agent and execution harness. Mezmo Governance is a separate, agent-agnostic product that is not open source and does not require AURA.
Is human approval required? Agentic infrastructure has no universal approval rule. In AURA’s documented approval gates, reads can proceed without approval, while configured tools that change production state wait for a human decision.
Evaluate the production boundaries
When evaluating agentic infrastructure for production SRE, check what an agent can read and change, who approves actions, and which investigation records you can inspect. To examine one SRE agent and execution harness directly, see AURA’s Apache 2.0 source code.
