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Agentic Marketing: What Engineering Figured Out First (And Why Marketing Is Next)

Something significant is happening in software engineering right now. Engineers are no longer just using AI to autocomplete lines of code. They’re running autonomous agents that plan work, execute it, verify the results, and move on to the next task — with minimal human involvement. Some engineers are running eight of these agents simultaneously, each tackling a different part of a project in parallel.

This shift has a name: agentic engineering. And the two concepts driving it — closing the agentic loop and parallel agents — are about to reshape marketing in exactly the same way.

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Engineering Saw the Change First

The debate in the engineering world is no longer about whether AI agents are useful. It’s about how autonomous they should be. As PulseMCP noted, the field is split between those who prefer short bursts of tightly supervised AI assistance and those who see the real unlock in running long, autonomous agents — often many in parallel.

The reality is both camps are right, and it depends on the complexity of the work. The frontier AI agents of today can autonomously build large parts of standard applications with minimal human input. More technically demanding projects still require more hands-on guidance.

Marketing is in a remarkably similar position. Some marketing tasks — updating meta descriptions, adjusting ad bids, sending follow-up emails — are well-defined, repetitive, and measurable. They’re the marketing equivalent of CRUD apps. Other tasks — brand strategy, creative direction, positioning — require deep human judgement.

The mistake most marketing teams are making right now is treating all of their work as if it requires the same level of human involvement. It doesn’t.

The Two Concepts That Matter

Two ideas from agentic engineering translate directly to marketing. If you understand these, you understand where marketing is heading.

1. Closing the Agentic Loop

In engineering, “closing the loop” means giving an agent the ability to verify its own work. An agent writes code, then runs the tests, checks whether they pass, and iterates if they don’t. The agent doesn’t just execute — it observes the outcome and self-corrects. Without this feedback mechanism, you just have automation. With it, you have an agent.

This is the critical distinction most people miss when they talk about AI in marketing. We’ve had marketing automation for over a decade — email sequences, scheduled social posts, rule-based ad bidding. But traditional automation is open-loop. It executes a predefined action and moves on. It doesn’t check whether the action worked. It doesn’t adapt.

Agentic marketing closes the loop. The agent takes an action, observes the result, evaluates performance against a goal, and adjusts its approach — all without waiting for a human to review a dashboard and make a decision.

Consider paid advertising. Traditional automation might increase your bid by 10% on Monday mornings because a rule told it to. An agentic system monitors performance in real-time, detects that a specific audience segment is converting well on mobile but poorly on desktop, reallocates budget accordingly, checks whether the reallocation improved ROAS, and continues iterating. The loop is closed — action, observation, evaluation, adjustment.

The same principle applies to content. An agent can identify a blog post where organic traffic has declined, analyse what’s changed in the search landscape, update the content to address new competing pages or shifting intent, republish it, and then monitor whether rankings and traffic recover. If they don’t, it tries a different approach. That’s a closed loop.

For email outreach, an agent can send a sequence, measure open and reply rates, identify which subject lines and value propositions are performing, adjust the messaging for the next batch, and keep refining. Each send cycle produces data that feeds directly back into the next one.

The key insight is this: closing the loop is what turns automation into agency. And the marketing tasks most suited to this are the ones with clear, measurable feedback signals — click-through rates, conversion rates, rankings, reply rates, ROAS.

2. Parallel Agents

The second concept from engineering is even more transformative. Engineers have moved from running one AI agent at a time to running multiple agents simultaneously. Simon Willison describes this as the “parallel coding agent lifestyle” — firing off several independent agents, each working on a different task, and reviewing the results as they come in.

Some engineers run three to eight agents in a grid layout on their screen, each committing its own changes to a separate branch of the codebase. As one engineer described it, this gives new meaning to the phrase “10x engineer.”

In marketing, parallel agents look like this:

Each agent operates independently, working towards its own objective, producing its own output. The marketer’s role shifts from doing the work to reviewing the results and providing strategic direction — the same shift that’s happening for engineers.

This isn’t theoretical. The pattern already works in engineering because the tasks can be clearly decomposed, the agents can operate on separate areas without conflicting, and the results can be independently verified. Marketing has exactly the same characteristics across many of its core functions.

What This Looks Like in Practice

Let’s get specific about the three areas where agentic marketing is most immediately applicable.

Paid media is arguably the most natural fit for agentic marketing. The feedback loops are tight (you see results in hours, not months), the metrics are unambiguous (ROAS, CPA, CTR), and the action space is well-defined (bids, budgets, audiences, creative).

An agentic system for paid ads doesn’t just follow rules. It:

The human’s role becomes setting strategy, defining constraints, and reviewing the agent’s decisions — not manually adjusting bids in a platform at 9am every morning.

Content Updating and Refreshing

Most marketing teams treat content as a “publish and forget” exercise. A blog post goes live, gets some initial traffic, and then slowly decays as the search landscape evolves around it. The teams that do refresh content do it manually — auditing posts in a spreadsheet, checking rankings, rewriting sections — and they can’t do it at scale.

An agentic content system closes the loop:

This is exactly the kind of well-defined, measurable, iterative work that agents excel at. The creative and strategic decisions — what topics to cover, what angle to take, what the brand voice should sound like — remain with the human. The grunt work of keeping 200 blog posts current doesn’t need to.

Email Cold Outreach

Cold outreach is a numbers game constrained by personalisation. The more personalised each email, the better the response rate, but personalisation doesn’t scale when a human is writing every message. Most teams compromise — they use templates with a few merge fields and accept mediocre reply rates.

Agentic outreach changes this equation:

The result is outreach that’s both more personalised and higher volume than any human team could achieve.

Why Now?

Three things have converged to make this possible:

  1. The models are good enough. Large language models can now read data, interpret context, make reasonable decisions, and generate high-quality output. They’re not perfect, but they’re good enough to handle well-defined marketing tasks with clear success criteria.

  2. The tooling exists. Platforms are emerging that allow agents to connect to marketing tools — ad platforms, CMS systems, email providers, analytics — and take actions within them. The infrastructure layer is being built.

  3. Engineering has proven the pattern. The agentic workflow isn’t hypothetical. Thousands of engineers are using it daily. The concepts — closing the loop, running parallel agents — are battle-tested. Marketing just needs to adopt them.

What This Doesn’t Replace

Agentic marketing doesn’t replace marketers any more than agentic engineering replaces engineers. What it does is shift the role.

Engineers using agents spend less time writing boilerplate code and more time on architecture, design decisions, and code review. The same shift is coming for marketing. Marketers will spend less time manually adjusting campaigns and more time on strategy, creative direction, and reviewing the output of their agents.

The marketers who will thrive are those who learn to manage agents effectively — defining clear objectives, setting appropriate constraints, reviewing results critically, and knowing when to override an agent’s decision. This is a skill, and it’s one that most marketing teams haven’t started developing yet.

The Bottom Line

The engineering world has already figured out that the future of work isn’t about using AI as a slightly faster tool. It’s about deploying autonomous agents that close their own feedback loops and operate in parallel.

Marketing is next. The tasks are there — paid media, content, outreach — and they share the same characteristics that make agentic engineering work: clear objectives, measurable outcomes, and well-defined action spaces.

The question isn’t whether this shift will happen. It’s whether your team will be early or late.

Growth Method is the growth operating system built for marketing teams focused on pipeline — not projects. Book a call to speak with Stuart, our founder, at https://cal.com/stuartb/30min.

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