Agent vs MCP vs Workflow

Markifact runs on a single engine of 500+ marketing operations, the same building blocks for pulling a Google Ads report, creating a Meta campaign, or writing to a Sheet. What changes is how you reach those operations. There are three surfaces:

• Workflows run operations as a fixed, visual sequence.
• Agents run operations conversationally, letting AI decide the steps.
• MCP exposes those same operations to an outside AI client like Claude or ChatGPT.

The operations are identical. You are only choosing who drives them: a canvas you built, an AI inside Markifact, or an AI in a tool you already use.

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Watch: which one should marketers use?

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The 10-second answer

• Same task, every week, no thinking required? Workflow.
• One-off or fuzzy task where you’d rather describe it than build it? Agent.
• You live in Claude, ChatGPT, or Cursor and want Markifact’s data there? MCP.

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Side by side

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Workflows

A workflow is an automated sequence you build by chaining nodes on a visual canvas. You define every step up front, then it runs the same way every time it’s triggered. Use a workflow when:

• The task repeats on a schedule (weekly report, daily sync, monthly rollup).
• You need the exact same steps every run, with no surprises.
• The logic is deterministic: pull this, transform that, send it there.
• You want it to run unattended, with no one in the loop.

Avoid a workflow when:

• Every run is different and hard to predict.
• You’d spend more time building the flow than just doing the task once.

Example: Every Monday at 9am, pull last week’s Google Ads performance, build a Slides deck, and post it to Slack. Build it once, and it runs forever.

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Agents

An agent is a conversational AI inside Markifact, purpose-built for marketing. You describe a task in plain language, and the agent figures out which operations to run, executes them, then surfaces the result for your approval before anything goes live. It is a complete marketing assistant, not just a chat box wired to some tools. What sets Markifact agents apart:

• Model agnostic, all in one place. Run on Claude, GPT, or Gemini, and switch the model per agent without changing how you work. No juggling separate apps for separate models.
• Bring your own API key. Plug in your own OpenAI, Anthropic, or Google key and the agent’s AI tokens are billed directly through your provider, with Markifact charging a flat 1 credit per message instead of model-based token pricing. See Bring Your Own Keys.
• Reach it anywhere. Chat from the web app, a whitelabeled Slack app, WhatsApp, or the API, all driving the same operations and connections.
• Built for marketers, not engineers. A marketing-friendly UI, results surfaced for review, and a human-in-the-loop approval step before anything publishes.
• Schedule it. Run a task on demand or set it to run on a schedule, so an agent can handle recurring marketing work without you starting the conversation each time.
• Skills, built in. Package your repeatable playbooks as skills that load on demand, and build new ones with the skill creator. No external setup, it’s part of the agent.
• Multiple agents, shared with your team. Create as many custom agents as you need, each with its own name, instructions, connected tools, and context (for example, one agent scoped to Client A, another to Client B), then share them with teammates so everyone works from the same setup.

Use an agent when:

• The task is one-off or open-ended (“audit this account and tell me what’s wrong”).
• You’d rather describe the goal than build the steps.
• The right steps depend on what the data shows.
• You want a human-in-the-loop checkpoint before changes publish.
• You want one marketing assistant your whole team can reach from Slack, WhatsApp, or the web.

Avoid an agent when:

• You need a strictly identical process to run unattended with zero variation (a workflow is more predictable and cheaper to repeat).
• The task is high-volume and mechanical, where a fixed flow is faster.

Example: “Pull last week’s Meta Ads performance, find the three worst-performing ad sets, and draft negative-keyword suggestions.” The agent decides the steps and asks before it changes anything. Schedule that same prompt to run every Monday and it becomes a standing report, no canvas required.

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Where agents fit inside workflows

The line blurs in one useful spot: the AI Agent node. It drops an agent into the middle of a workflow, so a mostly-fixed flow can make a dynamic decision at one step. Reach for it when most of your process is deterministic but one step needs judgment.

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MCP

MCP (Model Context Protocol) is a bridge. Instead of coming to Markifact, you stay in the AI client you already use, Claude, ChatGPT, Gemini, Cursor, or anything that supports MCP, and that client calls Markifact’s operations for you during a normal conversation. Use MCP when:

• You already work in an external AI tool and don’t want to switch contexts.
• You want to blend Markifact data with whatever else that AI is doing (analysis, writing, code).
• You’re a developer or a customer-facing app that needs Markifact operations programmatically per user.

Avoid MCP when:

• You want something to run on a schedule with no one present (use a workflow).
• You don’t already use an external AI client, in which case the in-app agent is simpler.

Example: From inside Claude, “Pull my GA4 sessions for last month, compare them with the month before, and write up what changed.” Claude calls Markifact for the data and handles the analysis itself.

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Agent vs MCP: the one that actually trips people up

Both are conversational and both run the same operations, so they look interchangeable. They aren’t. The difference is where the AI lives and what it comes with.

• An agent is a finished marketing assistant that lives in Markifact. The AI, the channels, the scheduling, the approval step, skills, shareable custom agents, and the marketing-tuned UI are all included.
• MCP is a pipe. It gives the operations to an AI client you bring (Claude, ChatGPT, Cursor), and that client supplies the AI and the interface.

So the question is rarely “which is more powerful.” It is “do I want Markifact to be the assistant, or do I want to plug Markifact into the assistant I already use?” Choose the agent when you want a turnkey marketing assistant the whole team can reach, with scheduling and approvals handled for you, and the freedom to pick or bring your own model. Choose MCP when you already work in Claude, ChatGPT, or Cursor all day and just want Markifact’s data and actions available there, alongside everything else that tool does.

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Pick by scenario

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They are not mutually exclusive

These surfaces share the same operations and the same connections, so they layer naturally:

• A workflow can call an AI Agent node for a dynamic step.
• An agent can do the exploratory work, and once you’ve nailed the steps, you rebuild it as a workflow to run on a schedule.
• MCP lets your external AI trigger Markifact while you keep building workflows in the background.

A common path: prototype with an agent, productionize as a workflow, and reach for MCP when you want it all from your own AI tool.

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FAQ

Agent and MCP both feel like chat. What's the real difference?

The agent is a complete marketing assistant that lives in Markifact: it brings its own AI (Claude, GPT, Gemini, or your own key), a marketing-built UI, channels (web, Slack, WhatsApp, API), scheduled tasks, and a built-in approval step. MCP is just a pipe that hands the same operations to an AI client you already use, like Claude or Cursor, where that client supplies the model and the interface. Pick the agent when you want Markifact to be the assistant; pick MCP when you want to plug Markifact into the assistant you already use. See Agent vs MCP above.

If an agent can do anything, why build workflows at all?

Workflows are deterministic, cheaper to run repeatedly, and need no one present. When a process is fixed and recurring, a workflow runs the exact same path every time with no token cost for reasoning. Agents shine when the steps aren’t known in advance or change run to run.

Do I have to set up connections separately for each?

No. Workflows, agents, and MCP all use the same connections in your workspace. Connect Google Ads once and every surface can use it.

How do credits differ across the three?

• Workflows charge per node that runs. Pulling a Google Ads report costs about 1 credit; account selection and field listing are free.
• Agents are priced on the AI model you use (token-based, with a multiplier per model, so a heavier reasoning model costs more than a lighter one). Bring your own API key and the AI tokens are billed through your provider instead, with Markifact charging a flat 1 credit per message.
• MCP charges per operation it runs, at the same rate as the matching workflow node. Pulling a report is about 1 credit; searching for operations, reading inputs, and listing connections are free.

A quick rule: pulling or writing data costs around 1 credit per action, while discovery and setup steps are free. See Credits & Usage for the full tables.

Can I start with one and switch later?

Yes, and it’s a good pattern. Explore a task with an agent, then once the steps are clear, rebuild it as a workflow to run on a schedule. Nothing is locked in, because they all sit on the same operations.

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