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Agentic workflow

Also known as: agent orchestration · multi-step agent

Definition

A process where an AI system is given a goal rather than a script, and chooses its own sequence of tool calls to reach it. Distinguished from automation by being non-deterministic: the same input can produce a different route each run.

Agentic workflows suit problems with too many branches to script — research, triage across messy inputs, drafting where the right structure depends on what is found. They are a poor fit anywhere the same input must produce the same output, because they will not reliably do that.

The practical constraint is verification. An agent that takes eleven steps to reach an answer has eleven places to be wrong, and the output arrives with the same confidence either way. Production agentic systems put a human gate before anything irreversible, and log every step so a wrong answer can be traced rather than guessed at.

Worked example

"Research this account, find the decision maker, draft an opening referencing their last announcement, and put it in drafts" — four tool calls the system sequences itself, with a human approving the send.

More in AI

AI agent

An AI agent is a system that pursues a goal across multiple steps using tools — reading data, calling APIs, updating records — rather than producing a single response to a single prompt.

AI automation ROI

The payback calculation for an automation: hours recovered multiplied by fully loaded hourly cost, against build plus running cost. Frequency dominates the result — the same build against a process running twice a month rarely pays back at all.

AI observability

Logging and monitoring for AI systems: what went in, what came out, which tools were called, what it cost, and how often a human overrode it. Without it, a misbehaving automation is undiagnosable.

AI readiness

Whether a business has the process documentation, data quality and decision ownership for AI to add value. Readiness is mostly organisational: the constraint is almost never model capability.

Automation debt

The accumulated cost of automations nobody owns: undocumented flows, forgotten triggers, and scripts that break silently when an upstream field is renamed. The AI-era version of technical debt.

Brand voice model

A brand voice model is a language model customised on a company's own copy, offers and guidelines so its output matches that brand's register, vocabulary and positioning without heavy editing.

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