GoHighLevel Managed Agents: Build AI Employees That Own Tasks End-to-End

When most operators think about AI in GoHighLevel, they picture a chatbot that answers questions or books appointments. That’s Conversation AI doing one job well. But Managed Agents are different. They’re AI employees that own entire tasks from start to finish—qualifying a lead, updating the CRM, creating follow-up tasks, escalating to a human when needed, and closing the loop. They don’t just respond. They execute, decide, and hand off with context.

If you’ve been running subaccounts for clients, you know the gap between a chatbot that replies and a system that actually completes work. Managed Agents fill that gap. They combine goals, tools, knowledge, and triggers into a single entity that can handle multi-step workflows without you wiring fifty automation nodes together. This post walks through what Managed Agents are, how they differ from Conversation AI and workflows, when to use them, and the practical setup order in a subaccount so you don’t break production while testing.

The Rename: Super Agents Are Now Managed Agents

HighLevel renamed Super Agents to Managed Agents in early 2025. If you see documentation or help articles titled “How to Build Managed Agents (formerly Super Agents),” that’s the same feature. The rename reflects what these agents actually do—they’re managed by you, the operator, and they manage tasks for your clients. The core functionality hasn’t changed, but the branding is clearer. Throughout this post, we’ll use Managed Agents exclusively, since that’s the current term in Agent Studio and the HighLevel interface.

Why does this feature matter now? Because clients expect AI to do more than chat. They want AI that qualifies leads overnight, updates pipeline stages, assigns tasks to sales reps, and escalates edge cases to a human with full context. Managed Agents let you deliver that without custom code or third-party middleware. You build the agent in Agent Studio, wire it to triggers in the subaccount, and it runs autonomously until it needs help.

Conversation AI vs Managed Agents: What’s the Difference?

Conversation AI in GoHighLevel is a chatbot layer. You configure it with a knowledge base, tone, and booking instructions, and it responds to inbound messages—SMS, webchat, Instagram DM, Facebook Messenger. It’s reactive. A contact says something, the bot replies. If the contact wants to book, the bot can trigger the booking widget. If the contact asks a question covered in the knowledge base, the bot answers. That’s the scope.

Managed Agents are proactive and task-oriented. They have goals, not just responses. A Managed Agent might have the goal “Qualify inbound leads and move qualified contacts to the Sales pipeline.” To achieve that goal, it uses tools—read contact fields, update custom fields, change pipeline stage, create a task for a sales rep, send a follow-up SMS. It doesn’t wait for the contact to ask the right question. It drives the conversation toward the goal, collects the information it needs, takes CRM actions, and hands off when the task is complete or when it hits a condition that requires human judgment.

Here’s a concrete example. A contact fills out a form and enters the system. Conversation AI might send a welcome message and answer questions if the contact replies. A Managed Agent, triggered by the form submission, sends a welcome message, asks qualifying questions, updates the contact’s Lead Source and Budget custom fields based on the answers, moves the contact into the Qualified stage of the Sales pipeline, creates a task assigned to the account manager with notes from the conversation, and stops. The agent owned the entire qualification workflow. The human picks up a qualified lead with context, not a raw form fill.

Goals and Instructions

Conversation AI has instructions—how to talk, what to avoid, when to offer booking. Managed Agents have goals and success criteria. You tell the agent what done looks like. “Your goal is to confirm the appointment time and send a reminder SMS 24 hours before. Success means the contact replied YES to confirm and the reminder is scheduled.” The agent knows when it’s finished. It won’t keep asking questions after the goal is met. It won’t wander off-topic. It’s focused.

Tools and CRM Actions

Conversation AI can read the knowledge base and trigger a booking flow. Managed Agents can do that plus use tools—read and write contact fields, read and write opportunity fields, change pipeline stages, add tags, remove tags, create tasks, create appointments, send SMS or email, search the knowledge base, and call custom webhooks. Every tool is a capability the agent can invoke to move toward its goal. You grant tools when you build the agent. If the agent doesn’t have the Update Contact tool, it can’t change custom fields. If it doesn’t have the Create Task tool, it can’t assign work to a human.

This is where Managed Agents become powerful. You’re not scripting every if-then branch. You’re giving the agent a toolbox and a goal, and the underlying model figures out which tools to use in which order based on the conversation and the contact’s data. It’s closer to practical AI agent design than traditional automation.

Knowledge and Context

Both Conversation AI and Managed Agents can use a knowledge base. The difference is how they use it. Conversation AI pulls answers to respond to questions. A Managed Agent pulls answers to inform decisions. If the agent’s goal is to qualify leads, it might check the knowledge base for your service pricing tiers, then ask the contact which tier fits their budget, then update a custom field based on the answer. The knowledge base isn’t just FAQ content—it’s reference material the agent consults while working.

You can also give a Managed Agent instructions that reference specific CRM fields. “Before you ask about budget, check the Lead Source field. If it’s ‘Referral’, skip the budget question and move straight to scheduling.” The agent reads the contact record, sees the field value, and adjusts its behavior. Conversation AI doesn’t have that level of CRM-aware branching without you building a separate workflow to feed it data.

Ownership of Multi-Step Work

Conversation AI handles one exchange at a time. Managed Agents own a sequence. They remember where they are in the task, what information they’ve collected, and what’s left to do. If the contact goes silent mid-conversation, the agent can pick up where it left off when the contact replies hours later. If the agent needs three pieces of information to complete the goal, it won’t mark the task done until all three are captured and written to the CRM.

This is the “end-to-end” part. The agent doesn’t just start the process. It finishes it, or it escalates with a clear handoff. You’re not stitching together a workflow that triggers the bot, then waits for a reply, then triggers another workflow to parse the reply and update fields. The agent does all of that in one execution because it owns the task.

When to Use Managed Agents vs Workflows vs Conversation AI Alone

Operators ask this constantly: when do I build a Managed Agent, when do I use a workflow, and when is Conversation AI by itself enough? Here’s a decision framework.

Use Conversation AI Alone When:

• The task is purely reactive—answer questions, provide information, offer booking.
• You don’t need CRM updates beyond what the booking flow handles automatically.
• The contact drives the conversation, and you’re fine with them asking anything.
• You want the simplest possible setup with the least maintenance.

Example: A contact asks about your hours, services, or pricing. Conversation AI pulls from the knowledge base and replies. No CRM action needed. No follow-up task. Just information exchange.

Use a Workflow When:

• The logic is deterministic—if this field equals that value, do this action.
• You need precise timing and sequencing—wait 2 days, send email, wait 3 days, send SMS.
• The task doesn’t require natural language understanding or conversation.
• You want full visibility into every branch and condition in the automation builder.

Example: A contact books an appointment. A workflow triggers, waits 24 hours, sends a reminder SMS, waits until 1 hour before the appointment, sends a final reminder. No conversation needed. The workflow executes on a schedule. For more on this pattern, see GoHighLevel follow-up automations.

Use a Managed Agent When:

• The task requires conversation and CRM actions together.
• You need the AI to collect information, make decisions, and update records based on answers.
• The task has a clear goal and success criteria, but the path to get there varies by contact.
• You want the agent to own the task from trigger to completion or handoff.
• You need context-aware escalation to a human when the agent can’t proceed.

Example: A new lead comes in from a form. The Managed Agent sends a welcome SMS, asks what service they’re interested in, asks their timeline, updates custom fields, moves them to the appropriate pipeline stage, creates a task for the sales rep with notes, and stops. The agent owned qualification. The human picks up a qualified lead ready for outreach.

Combine Them When:

You’ll often use all three. Conversation AI handles general questions. A Managed Agent handles lead qualification. A workflow handles post-qualification follow-up sequences. They’re not mutually exclusive. The key is knowing which tool owns which task. Don’t try to make Conversation AI do CRM updates by parsing replies in a workflow—that’s brittle. Don’t try to make a workflow have a conversation—that’s painful to build and maintain. Use each tool for what it does best.

Goals, Instructions, and Success Criteria for a Managed Agent

When you build a Managed Agent in Agent Studio, the first thing you define is the goal. This is a plain-language statement of what the agent is supposed to accomplish. Be specific. “Help leads” is too vague. “Qualify inbound leads by collecting service interest, budget, and timeline, then move qualified leads to the Sales pipeline and create a task for the sales rep” is specific. The agent knows what done looks like.

Instructions are how the agent should behave while working toward the goal. Tone, boundaries, what to say, what not to say. “You are a friendly intake assistant. Keep messages under two sentences. If the contact asks about pricing, provide the range from the knowledge base but do not negotiate. If the contact is rude, stay professional and escalate to a human.” Instructions shape the agent’s personality and guardrails.

Success criteria are the conditions that mean the task is complete. “Success means the contact’s Service Interest, Budget, and Timeline custom fields are populated, the contact is in the Qualified stage of the Sales pipeline, and a task is created for the assigned sales rep.” The agent checks these criteria after each action. When all are met, the agent stops and marks the task done. If the agent can’t meet the criteria—contact stops replying, contact says they’re not interested, contact asks a question outside the agent’s scope—the agent escalates or pauses.

Write these three elements before you configure tools or triggers. They’re your blueprint. If you can’t articulate the goal and success criteria in two sentences, the agent will drift. If your instructions contradict the goal, the agent will behave unpredictably. Clarity here saves hours of testing later.

Tools, Skills, Knowledge Base, CRM Actions, and Triggers

Once you’ve defined the goal, you grant the agent tools. In Agent Studio, tools are the actions the agent can take. The list includes:

• Read Contact: Pull contact fields—name, email, phone, tags, custom fields, pipeline stage.
• Update Contact: Write to contact fields—tags, custom fields, assigned user.
• Read Opportunity: Pull opportunity fields if the contact is linked to a deal.
• Update Opportunity: Write to opportunity fields—stage, value, custom fields.
• Create Task: Assign a task to a user with a due date and description.
• Create Appointment: Book an appointment on a calendar (requires calendar configuration).
• Send Message: Send SMS or email from the agent mid-task.
• Search Knowledge Base: Query the knowledge base for answers or reference data.
• Call Webhook: Trigger an external API or custom integration.

Grant only the tools the agent needs to achieve its goal. If the agent’s job is to qualify leads and create tasks, it needs Read Contact, Update Contact, and Create Task. It doesn’t need Update Opportunity or Call Webhook. Limiting tools reduces the risk of unintended actions and makes testing simpler.

Knowledge Base

The knowledge base is shared between Conversation AI and Managed Agents. You upload documents, FAQs, service descriptions, pricing sheets, policies—anything the agent might need to reference. The agent uses the Search Knowledge Base tool to pull information during a conversation. If the contact asks “What’s included in your premium package?” the agent searches the knowledge base, finds the answer, and replies. If the agent needs to know your cancellation policy before updating a contact’s status, it searches the knowledge base and uses that information to inform its decision.

Keep the knowledge base current. If your pricing changes and the knowledge base still has old numbers, the agent will give outdated information. Treat the knowledge base like documentation for a human employee—accurate, complete, and maintained.

CRM Actions and Field Mapping

Managed Agents read and write CRM fields directly. When you grant the Update Contact tool, you specify which fields the agent can modify. You might allow the agent to update Lead Source, Service Interest, Budget, and Timeline custom fields, but not Email or Phone. You control the scope.

Field mapping matters. If your custom field is named “Service_Interest” with an underscore, the agent needs to reference it exactly. If the field is a dropdown with options “Consulting”, “Training”, “Support”, the agent should write one of those exact values, not a paraphrase. Test field writes carefully. A typo in a field name means the agent thinks it updated the CRM but nothing changed.

Triggers: Messages, Appointments, Schedules, Events

Managed Agents don’t run automatically. You wire them to triggers in the subaccount. Common triggers include:

• Inbound Message: Contact sends an SMS or webchat message, agent replies and starts the task.
• Form Submission: Contact submits a form, workflow triggers the agent to start qualification.
• Tag Added: Contact gets a specific tag, agent starts a task (e.g., tag “New Lead” triggers the qualification agent).
• Appointment Booked: Contact books an appointment, agent sends a confirmation message and collects additional info.
• Pipeline Stage Change: Contact moves to a stage, agent starts a task (e.g., moves to “Negotiation” stage, agent sends contract and asks for signature).
• Scheduled Time: Agent starts a task at a specific time (e.g., daily at 9 AM, agent reviews all contacts in “Follow-Up Needed” stage and sends messages).

You configure triggers in the Automation or Workflow builder, not in Agent Studio. Agent Studio is where you build the agent. The subaccount automation is where you tell the agent when to run. This separation is important. You can build and test an agent in Agent Studio without it touching production contacts. Only when you wire a trigger does the agent go live.

Task Ownership Patterns: Qualify, Update CRM, Create Tasks, Escalate, Close the Loop

Let’s walk through a common task ownership pattern: lead qualification. This is where Managed Agents shine because the task has multiple steps, requires conversation, and ends with CRM updates and human handoff.

Step 1: Trigger and Initiation

A contact submits a form on the client’s website. The form submission triggers a workflow. The workflow adds the tag “New Lead” and starts the Managed Agent named “Lead Qualifier.” The agent’s goal: collect service interest, budget, and timeline, update the CRM, and create a task for the sales rep.

Step 2: Conversation and Data Collection

The agent sends an SMS: “Hi [First Name], thanks for reaching out! I’m here to help. What service are you most interested in—consulting, training, or support?” The contact replies “training.” The agent writes “Training” to the Service Interest custom field using the Update Contact tool. The agent continues: “Great! What’s your budget range—under $5k, $5k-$10k, or over $10k?” The contact replies “$5k-$10k.” The agent writes that to the Budget custom field. The agent asks: “When are you looking to start—this month, next month, or just exploring?” The contact replies “next month.” The agent writes “Next Month” to the Timeline custom field.

Step 3: CRM Update and Pipeline Movement

The agent now has all three pieces of information. It checks the success criteria: Service Interest, Budget, and Timeline fields are populated. The agent uses the Update Contact tool to move the contact from the “New Lead” stage to the “Qualified” stage in the Sales pipeline. It removes the “New Lead” tag and adds the “Qualified” tag.

Step 4: Task Creation and Handoff

The agent uses the Create Task tool to assign a task to the sales rep. Task title: “Follow up with [First Name] – Training inquiry.” Task description: “Service: Training. Budget: $5k-$10k. Timeline: Next month. Contact is qualified and ready for outreach.” Due date: tomorrow. The task appears in the sales rep’s task list with full context.

Step 5: Closing Message and Stop

The agent sends a final SMS: “Perfect! I’ve passed your info to [Sales Rep Name], who will reach out within 24 hours to discuss next steps. Thanks!” The agent marks the task complete and stops. It doesn’t keep messaging the contact. The human takes over from here.

This is task ownership. The agent started the conversation, collected the data, updated the CRM, created the handoff task, and closed the loop with the contact. The human didn’t have to monitor the conversation or manually update fields. The agent did the work.

Escalation Pattern

What if the contact replies “I’m not sure about budget yet” or “Can I get a custom quote?” The agent recognizes it can’t populate the Budget field with a standard value. It escalates: “No problem! Let me connect you with [Sales Rep Name], who can discuss custom options. They’ll reach out shortly.” The agent creates a task for the sales rep with notes: “Contact unsure about budget, requested custom quote. Needs human follow-up.” The agent adds the tag “Needs Human Review” and stops. The human picks up a warm lead with context, not a dead end.

Escalation is a tool, not a failure. Design your agents to escalate when they hit ambiguity, edge cases, or anything outside their scope. A good escalation includes context—what the agent tried, what the contact said, what’s blocking progress. The human should be able to pick up the conversation without asking the contact to repeat themselves.

Handoffs and Human Takeover: When, How, and What the Human Needs

Managed Agents are not set-it-and-forget-it. They’re part of a system that includes humans. The agent handles the repeatable, structured parts of a task. The human handles judgment calls, complex questions, and relationship building. The handoff is where this partnership works or breaks.

When to Hand Off

Design handoff conditions into the agent’s instructions. Common handoff triggers:

• The contact asks a question the agent can’t answer from the knowledge base.
• The contact expresses frustration or asks to speak to a human.
• The agent collects partial information but can’t complete the task (e.g., contact goes silent mid-conversation).
• The contact’s answers fall outside expected ranges (e.g., budget is $100k when your typical range is $5k-$10k).
• The task requires negotiation, customization, or approval beyond the agent’s authority.

Handoff is not surrender. It’s a planned transition. The agent should say something like: “That’s a great question—let me connect you with [Name], who can give you a detailed answer. They’ll reach out within [timeframe].” Then the agent creates a task with full context and stops messaging the contact.

What the Human Needs

When the human picks up, they need to know:

• What the agent was trying to accomplish (the goal).
• What information the agent collected (fields updated, answers received).
• Why the agent handed off (the blocking condition or question).
• What the contact is expecting next (timeframe, next step).

Put this in the task description or in contact notes. Don’t make the human read the entire conversation transcript to figure out where things stand. A good handoff note looks like: “Agent goal: Qualify lead. Collected: Service Interest = Training, Budget = TBD (contact requested custom quote), Timeline = Next month. Handoff reason: Custom pricing needed. Contact expects call within 24 hours.”

Human Takeover Mid-Conversation

Sometimes a human needs to jump into a conversation while the agent is still running. HighLevel allows this. The human can send a message from the conversation inbox, and the agent will pause. Configure your agent to recognize when a human has taken over—usually by checking if the last message sender was a user, not the agent. The agent should stay quiet until the human explicitly hands control back or closes the conversation.

Train your team on this. If a sales rep sees an agent conversation going sideways, they can step in, send a message, and the agent will defer. But if the rep doesn’t close the loop—doesn’t update the contact’s status, doesn’t create a task—the agent might resume later and cause confusion. Clear ownership rules prevent this.

Practical Setup Order in a Subaccount

Here’s the safe way to build and deploy a Managed Agent in a client subaccount without breaking production or spamming real contacts.

1. Define the Goal and Success Criteria

Write them down outside of HighLevel. One sentence for the goal, one sentence for success criteria, a short paragraph for instructions. Get client or team sign-off if needed. This is your spec.

2. Build the Agent in Agent Studio

Go to Agent Studio in the subaccount (or in your agency account if you’re building a reusable agent). Create a new Managed Agent. Name it clearly—”Lead Qualifier” or “Appointment Confirmer.” Enter the goal, instructions, and success criteria. Grant the tools the agent needs. Link the knowledge base if the agent will reference it. Do not wire any triggers yet. Save the agent as a draft.

3. Set Up Test Contacts

Create two or three test contacts in the subaccount. Use your own phone number or a test number that won’t annoy anyone. Tag them “Test Contact” so you can filter them out of reports later. These contacts are your sandbox.

4. Manually Trigger the Agent on a Test Contact

In Agent Studio or the conversation inbox, manually start the agent on one of your test contacts. Have a conversation. Answer the agent’s questions. Try edge cases—give incomplete answers, ask off-topic questions, say “I want to talk to a human.” Watch what the agent does. Check the contact record after each conversation—did fields update correctly? Did tasks get created? Did the agent stop when it should?

5. Review and Iterate

Read the conversation transcripts. Look for places where the agent misunderstood, repeated itself, or took the wrong action. Adjust instructions, refine tool permissions, update the knowledge base. Test again. Repeat until the agent handles your test scenarios cleanly.

6. Build the Trigger Workflow (But Don’t Activate It Yet)

Go to the Automation or Workflow builder. Create the workflow that will trigger the agent in production—form submission, tag added, inbound message, whatever. Add the action to start the Managed Agent. Add a filter so the workflow only runs on contacts without the “Test Contact” tag (so your test contacts don’t trigger production workflows). Save the workflow but leave it inactive or paused.

7. Test the Trigger Workflow on Test Contacts

Temporarily remove the “Test Contact” filter or create a separate test workflow. Trigger it on your test contacts. Make sure the workflow starts the agent correctly, the agent runs, and the task completes. Check logs. Confirm no errors.

8. Activate in Production with Monitoring

Activate the workflow. Monitor the first few live conversations closely. Check the conversation inbox, contact records, and task assignments. If something breaks, pause the workflow immediately, fix the agent, test again, then reactivate. Don’t let a broken agent run unsupervised for hours.

9. Document and Train the Team

Write a one-page doc for the client or team: what the agent does, when it runs, when it hands off, what the human should do when they get a task from the agent. Share example conversations. Make sure everyone knows the agent exists and how to work with it.

10. Schedule a Review

Put a calendar reminder for two weeks out. Review agent performance—how many tasks completed, how many escalations, any complaints or confusion from contacts. Adjust as needed. Agents drift over time as contact behavior changes, so plan for maintenance.

This order—build, test in isolation, test with triggers, activate with monitoring—prevents the nightmare scenario where an untested agent spams 500 contacts with broken messages at 3 AM. Always test on test contacts first.

Guardrails: What the Agent Must Never Do

Managed Agents are powerful, which means they can cause damage if misconfigured. Set explicit guardrails in the agent’s instructions and tool permissions.

Never Allow the Agent to:

• Delete contacts or opportunities. Agents should create and update, not destroy. If you need deletion logic, handle it in a workflow with human approval.
• Send messages outside business hours without explicit client approval. An agent that texts contacts at 2 AM will generate complaints. Use workflow scheduling or agent instructions to limit messaging windows.
• Negotiate pricing or make promises beyond its authority. If the agent says “I can give you 50% off,” and that’s not true, you’ve created a mess. Agents should provide information from the knowledge base, not invent offers.
• Access or modify fields it doesn’t need. If the agent’s job is lead qualification, it doesn’t need access to billing fields or opportunity values. Limit tool scope.
• Continue messaging a contact who has opted out or asked to stop. Build opt-out detection into instructions. If the contact says “stop,” “unsubscribe,” or “leave me alone,” the agent should stop immediately and tag the contact for human review.

Approval and Review Habits

For high-stakes tasks—sending contracts, confirming cancellations, processing refunds—don’t let the agent complete the task autonomously. Instead, have the agent collect the information, update the CRM, and create a task for a human to review and approve. The human makes the final call. The agent does the legwork.

Example: An agent handles cancellation requests. The agent asks why the contact wants to cancel, records the reason in a custom field, and creates a task: “Contact requested cancellation. Reason: [reason]. Review and process.” The human reviews, confirms the cancellation is legitimate, and processes it. The agent didn’t cancel the account on its own.

This pattern—agent collects, human approves—protects you from edge cases and mistakes while still automating the repetitive parts.

Testing Checklist and How Systems Rot If Unmaintained

Before you deploy a Managed Agent, run through this checklist on test contacts:

• Happy path: Contact provides all expected information, agent completes the task, CRM updates correctly, task is created, agent stops.
• Partial information: Contact provides some information but not all. Does the agent escalate or keep asking? Does it update the fields it has and create a task with notes?
• Off-topic question: Contact asks something unrelated to the agent’s goal. Does the agent answer from the knowledge base or escalate?
• Rude or hostile contact: Contact is rude. Does the agent stay professional and escalate, or does it argue?
• Opt-out request: Contact says “stop” or “unsubscribe.” Does the agent stop messaging immediately?
• Human takeover: You send a message mid-conversation. Does the agent pause and let you take over?
• Field write accuracy: After the conversation, check the contact record. Are all fields updated with the correct values? Are tags added/removed correctly? Is the pipeline stage correct?
• Task creation: Is the task assigned to the right user? Is the description clear and complete? Is the due date correct?
• No duplicate actions: Run the agent twice on the same contact. Does it create duplicate tasks or send duplicate messages?

If any of these fail, fix the agent before going live.

System Rot and Maintenance

Agents don’t stay effective forever without maintenance. Here’s what causes rot:

• Knowledge base drift: Your services, pricing, or policies change, but the knowledge base doesn’t. The agent gives outdated information.
• Field schema changes: You rename a custom field or change dropdown options. The agent tries to write to the old field name and fails silently.
• Contact behavior changes: Contacts start asking new questions or using new phrases. The agent doesn’t recognize them and escalates more often than it used to.
• Team turnover: The sales rep the agent assigns tasks to leaves the company. Tasks go to a dead inbox.
• Trigger changes: Someone edits the workflow that triggers the agent, adds a filter, or changes the tag, and the agent stops running without anyone noticing.

Set a recurring review—monthly or quarterly. Check agent performance metrics (if HighLevel provides them), read a sample of recent conversations, confirm the knowledge base is current, verify task assignments are going to active users, and test the agent on a test contact to make sure it still works as expected. Treat the agent like a team member who needs check-ins, not a fire-and-forget script.

Closing: Clear Next Steps

Managed Agents are the most powerful automation tool in GoHighLevel for operators who want AI to own tasks, not just respond to messages. They combine conversation, CRM actions, and task handoffs into a single entity that works autonomously within the guardrails you set. They’re not chatbots. They’re AI employees that execute, decide, and escalate.

If you’re ready to build your first Managed Agent, start with a single, well-defined task—lead qualification, appointment confirmation, or post-sale onboarding. Write the goal and success criteria in plain language. Build the agent in Agent Studio using the setup and use Managed Agents guide as a reference. Test on test contacts until the agent handles edge cases cleanly. Wire the trigger workflow, activate with monitoring, and document the handoff process for your team.

Don’t try to automate everything at once. Build one agent, learn how it behaves in production, refine it, then build the next one. Managed Agents are a new capability for most operators, and the learning curve is real. But once you’ve built a few and seen them handle tasks end-to-end, you’ll find dozens of places in your subaccounts where an agent can replace manual work, speed up response times, and free your team to focus on the conversations that actually need a human.

Start small, test thoroughly, and treat your agents like employees—give them clear goals, the right tools, and regular check-ins. They’ll handle the rest.

by Darius Gaynor