Agentic AI

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Glossary
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Agentic AI

Agentic AI refers to artificial intelligence systems that are semi- or fully autonomous, able to perceive, reason, and act on their own rather than only answering prompts. They perceive an environment, decide next steps, and run multi-step workflows with limited human intervention from on-call operators. Generative models return text or code for a prompt, while agentic systems choose tools, take intermediate steps, and stop when the stated goal is met.

Practitioners care because agentic systems change cost, risk, and engineering time, not only answer quality for buyers and operators. According to Gartner, 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5%. That adoption curve puts more automated action on production systems, so teams need authority bounds, auditability, and clear failure modes before autonomy expands.

Calling a large language model in a loop is not production-grade agentic AI for live operations at enterprise scale. Reliable systems need parallel investigation paths and hard limits on solo action versus human approval. Mature teams separate perception, causal investigation, and bounded action across alert detection, dependency walks, hypothesis tests, and policy-bound remediation or handoff. When applied to site reliability work specifically, agentic AI requires a continuously updated Production World Model™ of the environment to support autonomous investigation and remediation.

When agentic AI is applied to site reliability work, the result is an AI SRE: an agentic system built for production investigation and remediation. Across enterprise customers, Traversal delivers 82%+ accurate root causes in under five minutes on average as an observed result. It is the first and only AI SRE platform validated within the Fortune 100 at production scale.

Related

AI SRE

An AI SRE (AI Site Reliability Engineer) is an autonomous agentic system that performs causal investigation, root cause analysis, and remediation across production environments, operating as a continuously available teammate alongside human reliability engineers.

Agentic Mission Control

Agentic Mission Control is Traversal's term for the operating model in which an AI SRE continuously investigates the production environment, surfaces causal hypotheses before alerts escalate, and routes responders with the reasoning already assembled, enabling a level of autonomy and expertise previously unavailable to traditional mission control teams.

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard that defines how AI agents connect to external tools, data sources, and services, providing a uniform interface for agents to access the systems they need to act on a user's behalf.