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Glossary

Agentic Automation

Automation that uses AI agents to handle tasks requiring judgment — not just fixed steps, but decisions, tool use, and adaptation based on what the agent finds.

Agentic automation is automation powered by AI agents. Where traditional automation follows a fixed path, agentic automation can make decisions mid-task, adapt to what it encounters, and complete work that requires judgment — not just execution.

Agentic automation vs. traditional automation

Traditional automation — RPA, workflow automation, scheduled scripts — runs predefined steps. It’s fast and reliable when the task is consistent. It breaks when it isn’t.

Agentic automation handles variability. An agent can read a support ticket, determine what kind of issue it is, pull the right records, draft a response based on what it finds, and escalate if policy requires it. No two tickets follow exactly the same path — and that’s fine, because the agent decides rather than just executes.

The two aren’t opposites. Most production systems use both. Deterministic automation handles the predictable steps. Agents handle the parts that need judgment.

What makes automation “agentic”

Three things distinguish agentic automation from standard workflow automation:

  • Tool use — the agent can reach into real systems to read data, write records, send messages, or trigger actions, not just pass values between steps
  • Reasoning — the agent evaluates what it finds and decides what to do next, rather than following a fixed branch
  • Adaptation — if a step fails or produces an unexpected result, the agent can try an alternative approach rather than erroring out

The tradeoff is governance. An agent that can act in production systems needs audit trails, access controls, and clear scope. Human-in-the-loop checkpoints define where a person reviews before the agent proceeds.

Agentic automation at Tray.ai

Merlin Agent Builder is Tray.ai’s environment for building agentic automations — visual builder, multi-agent coordination, and guardrails baked in. Agents connect to 700+ systems through the same connector library that powers Tray.ai’s integrations, so they can act across the full enterprise stack, not just the apps they were trained against.

Agent Gateway governs how agents access those systems via MCP — controlling which agents can reach which tools, logging every action, and enforcing approval flows where policy requires them.

Apollo’s IT team built an agentic automation that handled real ticket resolution — not routing or drafting, but closing tickets end to end — and it was the top ticket resolver in its first month.

See how Agentic Automation works at Tray.ai

A tailored demo against your real systems.