General Terms

Agentic Workflow

A multi-step process carried out by AI agents that plan, use tools, and adapt to intermediate results with defined human checkpoints.

Definition

An agentic workflow is a process in which AI agents — systems built on large language models that can plan, use tools, and evaluate intermediate results — carry out a multi-step task with limited human intervention, adapting their actions to what they find rather than following a fixed script. It differs from traditional automation, which executes predefined rules, and from single-prompt AI use, which produces one output per request. In marketing production, agentic workflows chain steps such as reading a brief, drafting or editing creative against templates, rendering variants, checking outputs against specs, and preparing results for human review and approval.

Key Points

  • 1AI agents plan, use tools, and adapt to intermediate results — not a fixed rule-based script
  • 2Distinct from traditional automation (predefined rules) and from one-shot generative AI (single prompt, single output)
  • 3Effective deployments keep humans at defined checkpoints: strategy in, approval out
  • 4In ad production, agents operate structured substrates — briefs, data, and code-defined templates — end to end

Examples

An agent reads a campaign brief, drafts hook variants, edits a coded video template's props, renders, and queues output for approval

A reporting agent pulls platform metrics, flags creative fatigue, and proposes which ads to refresh with supporting evidence

A localization agent adapts a master campaign into five markets, checking each version against placement specs before handoff

A research agent assembles competitor-creative summaries into a structured input for the next creative sprint

How AdSights helps with Agentic Workflow

The AdSights Ads Framework was built for agentic operation: its video ad templates are code, and its bundled Claude Code skills give an AI agent the structured actions — draft variants, edit template props, batch-render, check specs — that a production workflow needs. The human stays at the two ends that matter: the brief going in and the approval coming out. Paired with performance data, the same loop closes end to end — the agent proposes refreshes based on what is actually fatiguing or winning in-market.

Want this workflow for your own ad production?

Explore the Ads Framework

Supplemental Resources

Frequently asked questions

Common questions about Agentic Workflow, answered.

What is an agentic workflow?
An agentic workflow is a multi-step process executed by AI agents — LLM-based systems that can plan a task, call tools (search, code, APIs, renderers), evaluate intermediate results, and adjust course — with humans intervening at defined checkpoints rather than at every step. The defining property is adaptation: instead of following a fixed script, the agent decides its next action based on what the previous action produced.
How is an agentic workflow different from regular automation?
Traditional automation executes predefined rules: if X, do Y, the same way every time — powerful for stable, repetitive processes but brittle when inputs vary. An agentic workflow substitutes a reasoning model for the fixed script: the agent interprets the goal, chooses tools and steps, handles variation in inputs, and self-corrects when a step fails. The practical difference is scope — automation handles the predictable middle of a process, while agents can also handle the judgment-adjacent steps around it, within limits you set.
What is the difference between an AI workflow and an AI agent?
A useful engineering distinction (drawn by Anthropic among others): in a workflow, the developer predefines the sequence and the model fills in steps; in an agent, the model itself directs the sequence — deciding what to do next based on results. 'Agentic workflow' spans this spectrum in practice: many production systems are predefined pipelines with agent-directed segments inside them, which delivers most of the value with more predictability than a fully open-ended agent.
How are agentic workflows used in advertising?
The strongest current uses are production and analysis. On production: agents operate template systems — reading a brief, drafting copy variants, editing code-defined ad templates, batch-rendering sizes and versions, and checking outputs against platform specs before human review. On analysis: agents assemble performance summaries, flag creative fatigue, and propose next tests. Delivery-side optimization (bidding, budgets) remains largely platform automation; the agentic layer is what is changing on the make-the-ads side.
What are the risks of agentic workflows, and where do humans stay in the loop?
The main risks are compounding errors across steps, confident-but-wrong outputs, and actions taken outside intended bounds. Production deployments manage these structurally: constrain what the agent can touch (templates, staging systems — not live spend), make its work inspectable (code diffs, rendered previews, logs), and place human approval gates before anything ships or spends money. The reliable pattern keeps strategy, brand judgment, and final sign-off human while the agent does the multi-step execution in between.

Related Terms

Marketing Automation

Related term

general, similar

AI-Generated Creative

Related term

creative, component

Creative Automation

Related term

creative, similar

Ads as Code

Related term

creative, component

Automated Campaign Management

Related term

creative, similar