Agentic Workflow
Agentic Workflow
What is an agentic workflow?
- Multi-step AI that plans, executes, and checks in a loop
- Not a single answer, but 'getting work done'
- Automating complex work
Adopting an agentic workflow changes what the person in charge manages. They used to spend time polishing prompt wording. Now they design how many steps to split the work into and what criteria decide each step is 'done.' The skill that matters shifts from writing flair to breaking work down. If the team members who do the work can explain its sequence and checkpoints in plain words, that explanation becomes the blueprint.
One distinction is worth making. A fixed-order “chain” is different from an “agent,” where the AI chooses its next action based on the situation. For work that stamps out the same form every month, a chain is cheaper and safer; for research where sources differ every time, an agent is the right fit. Don’t overlook that completion probability falls multiplicatively as steps increase. Even at 90% accuracy per step, chaining five steps gives roughly a 60% chance of getting through to the end. That’s why, in practice, it’s more stable to cap retries at 2–3 and keep the number of steps within five.
You also need criteria for where to apply it. For work where each case calls for a different judgment and the volume is low, automating it just adds maintenance effort. Tasks that repeat 30+ times a month and where good and bad results can be told apart in writing come first. Because calls pile up at every step, costs can run several times higher than a single query, so calculate the cost per case first and set it next to the labor cost of doing the work by hand.
The most common misconception is the expectation that “if you just hand over a goal, it will see it through on its own.” In practice, when search results come back empty, it repeats the same query and racks up cost, or fills in unsupported sentences that sound plausible and marks the task “done.” Give it email-sending and accounting-entry permissions all at once and you risk an incident where a wrong draft goes straight out to customers. Build step-by-step logs and rollback points first, and require human approval for any step involving money, external sending, or HR.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Seolyeong ParkGenerative AI and AX training specialist with 17 years of hands-on experience and 200+ lectures, helping non-technical staff put AI to work right awayIt evolved from research on combining planning, tool use, and self-checking in LLMs (such as ReAct in 2022), and as AI agents and multi-agent collaboration became practical, the term “agentic” came into wide use.
Consider a hypothetical case. Company A imports and distributes industrial parts, with 180 employees and KRW 42 billion (≈ US$31,423,000) in annual revenue. Two junior associates on its overseas sales team spent 14 hours a week opening some 120 notices across three procurement sites, one by one, just to pick out the ones worth bidding on. After they missed two or three bid deadlines every quarter, the company launched a three-month improvement project.
- Manager Kim of the overseas sales team narrowed the scope, saying 'Let’s not automate everything; let’s stop at screening notices,' and split the work into four steps: collection, first-pass screening, summarization, and review.
- A collection agent sweeps the three sites and a screening agent checks the results against the company's 42 product lines, cutting the weekly review load from 120 notices to 18.
- The review agent runs a 7-item checklist covering delivery dates, warranty terms, and more. If any item is still blank after up to three rounds of correction, it flags 'needs confirmation' and stops.
- In week two of the rollout, a draft came out with an amount 10 times too high after the AI misread a unit price. Team Lead Park decided that 'people check the numbers' and required a source link next to every amount.
- Three months later, screening work that had taken 14 hours a week dropped to 3 hours a week, and quarterly bid participation rose from 9 to 21 with zero missed deadlines.