An autonomous AI agent platform: you give an agent a goal in plain English and it plans, uses real tools, and delivers the finished work.

Is there a free plan?

Yes. Actus has a free plan to get started, with paid plans that add higher limits and advanced features like scheduled automations and team workspaces.

Can agents run automatically on a schedule?

Yes — agents can run hourly, daily, or on a custom cadence, so routine work happens automatically without you starting it each time.

How is Actus different from a chatbot?

A chatbot replies with text. An Actus agent takes action: it uses real tools to research, write, build, and send, then returns a finished deliverable instead of just advice.

Do I need to know how to code?

No. You describe your goal in plain English and the agent plans and executes the steps. Agents can write and run code when a task needs it, but you never have to.

Do I stay in control of what an agent does?

Yes. Irreversible actions require your approval, every run has budget and step limits, and each step is logged so you can see exactly what happened.

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Build Your First AI Agent Workflow

Actus · October 4, 2026

AI agentsworkflow automationgetting startedimplementation

How to Build Your First AI Agent Workflow

Building an AI agent workflow is not about mastering a new programming language. It is about defining a clear task, writing explicit instructions, and testing outputs before connecting the workflow to live systems. This guide walks through building a first workflow from problem to deployment.

Step 1: Name the problem

Start with a specific, repeating problem. Do not start with "automate my business." Start with "I spend two hours every Monday researching ten leads" or "I draft the same type of follow-up email twenty times a week." A clear problem leads to a clear workflow.

Step 2: Define the inputs and outputs

List what the workflow receives and what it produces. For lead research, the input might be a list of company names and locations. The output might be a spreadsheet with website, services, contact information, and a fit score. Be specific. Vague outputs lead to vague results.

Step 3: Write the success criteria

How will you know the workflow worked? Define quality standards. For lead research, success might mean: every record has a website or a "not found" note, services are extracted accurately, fit scores match your manual judgment on test cases, and the workflow completes within a time budget.

Step 4: Build the workflow

Most AI agent platforms let you describe the workflow in natural language. Write clear, detailed instructions. Specify where to find information, what to do when data is missing, how to score fit, and what to save. Include examples. The more explicit the instructions, the more consistent the results.

For a lead research workflow, the instructions might include: search for the company website using the business name and location, visit the website if found, extract the services from the Services page or homepage, note any visible gaps like missing contact forms, score fit based on service area and service type, and save the result with the source URL and timestamp.

Step 5: Test on historical data

Run the workflow on ten examples you already know the answer for. Review every output. Check for accuracy, consistency, and edge cases. Common issues include misreading ambiguous websites, skipping required fields, and scoring incorrectly. Fix the instructions and re-test.

Step 6: Add guardrails

Define what the workflow should not do. Do not send emails without approval. Do not invent facts. Do not skip verification. Do not proceed if a required field is missing. Guardrails prevent expensive mistakes.

Step 7: Connect to your systems

Once the workflow is accurate, connect it to your lead source and CRM. Start with a small batch. Monitor results daily. Look for errors, exceptions, and unhandled cases. Refine the instructions and expand.

Step 8: Schedule or trigger it

Decide when the workflow runs. It might run on a schedule like every Monday at 9 AM, or it might trigger when a new lead arrives. Schedule it and let it run for a week. Review outputs and measure time savings.

Example: lead research workflow

Problem: A sales team spends ten hours per week researching fifty local contractors.

Inputs: List of business names and locations from Google Maps.

Outputs: Spreadsheet with website, services, contact information, and fit score.

Success criteria: Ninety percent of records have accurate website and services data. Fit scores match manual judgment on test cases. Workflow completes in under one hour.

Instructions: For each business, search for the website, visit it, extract services and contact information, score fit based on service offerings and service area, and save the result.

Test: Run on ten known contractors. Verify accuracy. Fix instructions.

Guardrails: Do not invent services. Mark missing data as "not found." Do not contact prospects without approval.

Connect: Link to the weekly Google Maps scrape and save results to the CRM.

Schedule: Run every Monday at 9 AM.

Result: Research time drops from ten hours to one hour of review.

Common first workflows

Lead research and qualification
Weekly competitor monitoring
Website audit for prospects
Cold email drafting with personalization
Proposal content refresh
Client reporting automation
Social media monitoring and response drafting

Mistakes to avoid

Do not start with a complex workflow. Build one narrow workflow first. Do not skip testing. Do not connect to live systems before verifying accuracy. Do not automate external actions without approval. Do not assume the workflow will handle every edge case perfectly. Monitor and refine.

Conclusion

Building your first AI agent workflow starts with a clear problem, explicit instructions, and careful testing. The result is a repeating task that runs automatically and consistently, freeing your time for decisions and relationships. Explore agent workflows at https://actusagent.cc.