Agent-Ops 0.4.0 Released: Methodology for Secure Human-AI Collaboration in IT Operations
Agent-Ops 0.4.0 introduces a structured methodology for collaboration between engineers and AI agents in IT operations and support. The project, founded by Sergey Zhitinsky of Git in Sky, has released its first public normative candidate on GitHub and GitVerse. Two additional companies have become maintainers after agreements reached at the IT Elements 2026 conference, making the effort a joint industry initiative rather than a single-company project.
The methodology responds to growing use of AI agents for log analysis, configuration drift detection, root-cause investigation, and change preparation. It highlights the gap between an agent convincingly explaining a problem and the safe execution of its recommendation. Key concerns include whether the agent used current data, confused environments, verified hypotheses, or received malicious instructions through processed inputs. Responsibility for infrastructure changes must remain with humans—service owners, managers, and engineers—because accountability cannot be shifted to a model.
Agent-Ops enforces a clear separation of roles. A deterministic collector gathers facts within approved boundaries and records provenance, timestamps, and completeness. The agent correlates facts, generates testable hypotheses, and proposes a plan. An authorized human reviews and approves or rejects the specific plan. A separate deterministic executor then applies only the approved actions after re-checking permissions and conditions. The agent has no direct path from its output to live system changes.
The process consists of eight steps: Intention, Evidence, Diagnostics, Plan, Approval, Controlled Change, Verification, and Lessons Learned. Each step maintains distinct records so that any final change can be traced back to its supporting data, decision, and verification. The principle “unknown ≠ OK” requires that unverified facts remain marked as unknown rather than assumed correct.
Three independent planes—data, governance and policies, and independent verification—must each be satisfied separately. The Guardian role performs independent checks on data freshness, rule compliance, and authorization. These checks can be automated for deterministic conditions, while the agent may assist only with semantic consistency. Technical capability, autonomy, and potential impact are treated as separate properties that cannot be collapsed into a single trust level for the AI.
Version 0.4.0 is published as a candidate for community review. It includes a white paper in Russian and English, a glossary, a standards map, and machine-readable schemas. The project invites engineers, architects, support managers, and service owners to contribute via pull requests, issues, or new operational scenarios.
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