On this page
An agentic workflow is one where an AI agent observes buyer signals, resolves them to one account, works out what they mean, decides on an action and takes it across the CRM, the ad platforms and the outreach tools, with a person approving the steps that carry risk. That is the definition. What follows is one such workflow, end to end, built from signals every B2B team already collects and mostly ignores.
Four days, four signals
Monday: someone from a target account clicks a LinkedIn ad and reads a comparison page. Tuesday and Wednesday: three people from the same account visit the site, two of them landing on pricing. Thursday: a champion who has been silent for a month replies to a rep's email. Friday: a discovery call with a technical evaluator is recorded and transcribed.
In most stacks those four events live in the ad platform, the web analytics tool, the email client and the call recorder, about four people who appear to have nothing to do with each other. No one sees them together. Nothing happens until the rep notices the reply, which might be Thursday or might be the week after.
Gartner's May 2026 buyer research says buyers used seven information sources on average in a recent purchase, and 69% went to a rep to validate what AI had told them. Seven sources is seven signals in seven systems. The rep who turns up with the right context at the right moment is the validation the buyer is already looking for. The workflow below is how the rep gets the context.
Observe
The agent's first job is to notice. It reads every source continuously: ad engagement from LinkedIn and Google, first-party web visits, email and calendar, transcripts, marketing automation events, product usage, CRM changes. This part is cheap. Every tool already does it for its own data. The difference is that one thing is watching all of them at once.
Unify
Before it can reason, the four signals have to become one story. Identity resolution matches the ad click, the three sessions, the email reply and the transcript to the same account, then to the people, then to the open opportunity and the campaign journey that led to it.
This is the step most agent deployments skip and it is why most of them fail. An agent reading unresolved data acts on three versions of the same buyer, and it does so with total confidence. We do the resolving once, on the Full Funnel Data Graph, so every agent inherits it instead of redoing it.
After unification the agent knows: one account, four engaged people, two of them not yet in the CRM, one open opportunity at evaluation, a champion back in play, a technical evaluator active, and two economic-buyer personas nobody has touched.
Infer
Now the models run. Account momentum is up sharply over 14 days. Transcript sentiment is positive with one open objection about integration. Threading has gone from one contact to four. Pipeline propensity for the opportunity has moved from middling to high, and the close date the rep typed in a month ago suddenly looks plausible.
None of that was computed by the agent. The propensity, the momentum and the sentiment came back from the predictive models as finished outputs. The agent's job is to read them together, which is a very different job from calculating them.
Decide
It proposes four things, each with the evidence attached.
Outreach to the two economic-buyer personas who have not engaged, with a message that references the integration objection from the transcript. The account into the acceleration audience, so the buying group starts seeing product-proof creative rather than awareness creative. A task on the opportunity for the AE: confirm the integration path with the technical evaluator before the next call. And more spend on the LinkedIn campaign that produced the original click, because attribution shows it opening doors at accounts that look like this one.
Activate
The actions land in the systems that own them. The CRM gets the task and the two new contacts. The ad platform gets the audience change. The outreach tool gets the sequence. The campaign gets the budget change.
Three of the four commit under standing approval. They are low-risk and reversible. The spend increase waits for a person. On the GTM Harness that is Propose, Approve, Commit, Roll back, and it is the line between an agentic workflow and an automation that fires whether or not anyone is watching. Gartner's May 2026 governance note predicts 40% of enterprises will demote or decommission autonomous agents by 2027 over governance gaps. The approval step is how you stay out of that 40%. We have not found a shortcut.
Two weeks later
The economic buyer takes a meeting. The integration objection gets closed. The opportunity moves to negotiation. The workflow's own touches are stored as attributable events alongside every other touch, so the attribution model can say what the outreach, the audience change and the spend increase each contributed, and the next decision is better for it. This is the part no standalone agent produces, because a standalone agent only ever sees its own conversations.
What it takes
Identity resolved across every signal source, so four events become one story. Intelligence computed and returned to the agent rather than recalculated by it. Actions governed, with approval where the risk is and rollback when a decision was wrong. And a record of what each action did to pipeline, so the loop actually closes. A marketing operations leader at a software supply chain company described working this way as picking through a crowded journey, "a needle in a haystack a little bit," with the system "helping me pick through that haystack. In general, it's feeling powerful."
Observe everything. Unify it once. Infer with models. Decide with evidence. Act under approval. Measure what the action did. That is the whole thing.
Frequently asked questions
What is an agentic workflow?
One where an AI agent observes signals, decides what to do and does it across systems without a person routing each step, while a person approves the steps that carry risk. In GTM the signals are buyer behaviour and the actions are CRM updates, audience changes, outreach and spend.
What is the difference between an agentic workflow and marketing automation?
Automation runs a fixed rule: form filled, sequence sent. An agentic workflow reads several signals together, resolves them to one account, works out what they mean and picks among several actions. When the signals change, so does the decision.
Which actions should an agent take without approval?
Low-risk, reversible ones with a standing policy behind them: adding an account to an audience, creating a CRM task, logging a signal. Anything that reaches a prospect or moves money should be proposed and approved, at least until the agent has a track record on that action type.
Why does an agentic workflow need a context layer?
Because the signals arrive from different systems about what look like different people. Without identity resolution the agent treats an ad click, a web visit and an email reply as three unrelated events. The context layer resolves them to one buying group and one opportunity before the agent reasons about anything.
How does RevSure run agentic workflows?
Agents on the GTM Harness read the Full Funnel Data Graph, call the predictive models for momentum, sentiment and propensity, and act through Propose, Approve, Commit and Roll back. External assistants run the same flow through the RevSure MCP server.
Related reading: what is agentic GTM, MCP tools for GTM: the four primitives, AI agent orchestration.