Since launching Quotient, we've watched people use our agents for all kinds of marketing work: writing blog posts, planning campaigns, researching competitors, analyzing SEO performance. Most of this happens in one-off conversations. You ask the Blog Agent to research a topic, it does, and that's that. The next week, you ask it again.

We kept hearing the same thing: "I wish the agents could just do this automatically every week." So we built Agent Jobs. It's a way to schedule agent interactions to run on a recurring basis—weekly, monthly, whatever cadence makes sense for your work.

How is using Agent Jobs different from a regular conversation? When you create a job, you give an agent a specific prompt, choose when it should run, and optionally attach files for context. The agent then executes that prompt automatically on your schedule. It can search the web, analyze data, create content, update your Knowledge Store—anything it would do in a normal conversation. Once the job completes, you get a notification with a summary of what happened.

The key difference from traditional marketing automation is what's actually being automated. Legacy platforms automate actions: send this email, update this field, trigger this workflow. Agent Jobs automates intelligence: research competitive positioning changes, suggest blog topics based on trending keywords, analyze campaign performance and recommend adjustments. The agents use their full toolkit—web search, content generation, strategic reasoning—to complete work that would be tedious to repeat manually but valuable to do consistently.

What you can do with it

The most common use cases we've seen so far:

• Weekly SEO analysis: The Blog Agent researches keyword trends, identifies content gaps, and suggests new blog topics based on what's actually ranking.

• Competitive intelligence: The Campaign Agent monitors competitor websites and positioning, tracking changes in their messaging, product launches, or market moves.

• Content planning: Monthly prompts asking agents to suggest podcast topics, blog themes, or social media series based on industry trends and past performance.

• Campaign tracking: Weekly reports on which deliverables are on track, which are behind schedule, and what needs attention before launch dates.

• Executive social presence: Regular research on trending topics and industry conversations that could inform CEO or executive posts on LinkedIn and X.

Each job creates a persistent thread, so agents can reference previous runs and track changes over time. A weekly competitive intelligence job doesn't just report what's happening now—it can compare against previous weeks, identify trends, and highlight what's actually changed. This accumulated context compounds in value the longer a job runs.

How it works

From an engineering perspective, Agent Jobs is built directly on top of our existing agent infrastructure. When a job runs, it creates a full agent session with access to the same business context, Knowledge Store, and tools that the agent would have in a live conversation. This means we didn't need to build a separate automation engine or create limited versions of our agents for scheduled work—scheduled executions are just regular agent interactions triggered by a cron scheduler instead of a human message.

The technical challenge wasn't scheduling itself—that's solved. The challenge was ensuring scheduled agents have proper context and can work autonomously without constant human intervention. We solved this through a combination of persistent context (files you attach to jobs), business knowledge (automatic Knowledge Store access), and accumulated memory (agents can reference their own previous job executions in the same thread).

What's next

Agent Jobs represents a shift from automating actions to automating intelligence. Traditional marketing automation excels at triggering predefined workflows. Agent Jobs excels at recurring strategic work that requires research, analysis, and adaptation to changing contexts.

We're still learning what works best. Early feedback suggests people want more sophisticated scheduling options, better ways to chain jobs together, and improved visibility into what agents are doing during long-running jobs. We'll be iterating quickly based on what we see people actually using this for.

This is the beginning of something larger: software that doesn't just execute tasks on a schedule, but applies genuine intelligence to recurring work. That's the kind of automation that actually scales strategic thinking rather than just operational tasks.