Agent Studio

Please implement this ASAP!!! Grok Bot Mimic
What GHL AI Agents Are Missing Agent-to-Agent Communication: AI agents should be able to communicate, collaborate, and delegate tasks to one another. Here i don't mean the subagent ghl agent creates itself for its ease here im talking about my seperate agent i created it should able to actively tag @ mention it in its chat inbox and both can discuss whatever instructions i give them! Shared Global Memory: Every agent should have access to shared memory and know what information is stored in other agents' files. Centralized File Access: We should be able to view, access, and manage files stored by individual agents. Remote Computer Access: We should be able to open an agent's computer anytime and see exactly what it's working on. Built-in Terminal: Agents should have terminal access to execute commands, run scripts, and perform technical tasks. Built-in Browser: We should be able to open Google Chrome inside an agent's computer and watch its browser activity. Persistent Login Sessions: We should be able to give agents access to social media accounts, websites, and other platforms without repeatedly entering credentials. Multi-Agent Teams: We should be able to create groups of AI agents dedicated to specific tasks, with each agent having a defined role while they all can interact just like real employees wihtout needing real human. Task Delegation: Agents should be able to assign subtasks to other agents, exchange results, and coordinate workflows independently. Real-Time Monitoring: We should have visibility into what each agent is doing, which files it's accessing, and what tasks it's completing. -We should be able to interrupt the browser activity and continue doing work clicking whatever oursleves in agent's browser -We should be able to have a record button so if browser agent doesn't understand anything we record clip on agent's browser inside its computer and give it so it learns overtime -We should be able to open any agent's computer even if its not doing any active task and is at rest so we can see what tabs have been opened in its google chrome and what files it have stored or activity in its terminal Just literally mimic copy the grok bots interface and its functionality.
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🤖 Share an Agent Studio agent via private link with a contact or company, for conversational briefings and onboarding with AI
THE PROBLEM When a customer signs, the first thing we do is ask them for information. Brand, access, objectives, competence, tone, audience, visual references. Today that is done in two ways and both are bad. Or we send you a long form. He opens it, sees twenty fields, leaves it half done and has to be chased for three days. You fill in open fields with a line because you don't know what we expect. Or we set up a one-hour kick-off meeting to get the information out of you by talking. It works better, but it spends a senior's hour on something that's basically asking questions from a list and taking notes. Agent Studio already knows how to do that. What's missing is the door through which the customer enters. HOW DOES AGENT STUDIO WORK NOW A Managed Agent runs in an internal chat or triggered by a trigger. The panel makes it clear: Triggers like “Chat Started” (internal) or “Customer replied”. All of this is designed to be used by a person in the agency from the sub-account or to take place on third-party channels. The other half is missing. There is no place where the end customer talks to the agent through the same channel as the agent (Agent chat or similar), nor where we read that conversation. WHAT DO WE ORDER That a published agent can be shared with a contact or an opportunity through a private link, so that someone outside the sub-account can talk to him from the browser and everything is recorded in the CRM. YOUR OWN AGENT SAYS SO I asked the Agent Studio agent if I could set this up. He answered me, literally: “Briefing and Onboarding, starts when you open a chat; I can't confirm that this link alone offers these functions: Linking and history: associate each session with a contact or opportunity and show the entire thread from the sub-account. Files and continuity: allow attachments, save them to the contact and resume the same interview another day. Link control: assign it to a contact, expire it, revoke it or protect it with a code. Closing and automation: save a report with answers and files, send it by email and launch workflows for events such as “briefing completed” or “abandoned”. Those link-specific events are not confirmed as available triggers. I would not present those benefits as having already been resolved. For a first version, I would use a form or workflow associated with the contact.” Your own agent ends up recommending you leave Agent Studio and return to the usual form. There's the missing product. WHAT PROBLEM DOES IT SOLVE When a customer signs, the first thing is to extract information from them. Brand, objectives, competence, tone, audience, access, visual references. Today there are two paths and both of them cost money. We sent you a long form. Twenty fields, he leaves it half done, you have to chase him for three days, and he fills in the open fields with a line because he doesn't know what we expect from him. Or we set up a one-hour kick-off. It works out better, and we get an hour away from a senior asking questions from a list and taking notes. A trained agent does the same thing: he asks questions, asks questions when the answer is vague, asks for the missing files and does not close until he has what we have asked him to collect. The engine is already built inside Agent Studio. What is missing is the door through which the customer enters. WHAT SHOULD I INCLUDE Session tied to the CRM. The link is generated from a contact, a company or an opportunity, and everything that happens there hangs on that record. Thread visible from the sub-account. A section where you can read the entire conversation: what the agent asked, what the customer answered, when, what went up, where did he stay. Today that section does not exist for any Managed Agent execution. Files uploaded to the conversation. Logos, brand manuals, photos of the place, PDFs, captures, whatever the agent asks for. Each file is saved in two places at once: in the contact or company file, within their documents, and in the agent's own private thread, at the point of the conversation where it was uploaded. This way, the team finds it in the file when looking for a single logo, and sees the full context when reading the thread, knowing what question that file answered. That it can be resumed. The customer answers eight questions, closes the laptop, returns tomorrow to the same link and continues where he left off. Control over the link. Expiration, revoke it whenever we want, optional access code. One per contact, never a public link going around. Report when finished. A document with the answers ordered by blocks and the files received, saved in the contact and, if we want, sent by email to the team. MCP with Google Drive tools to create Google Docs files, folders or documents. New triggers in Workflows. Open link, session completed, session abandoned, file received. With that, we set up the automatic reminder to the one that doesn't end and the internal warning to the one that does. No passwords per chat. The agent should not ask for access or keys in the conversation. That block has to be derived to a secure method. USE CASES: * Full brand briefing before touching a design. * Post-sales onboarding: tax data, contact persons, schedules, inventory of what they already have assembled. * Kick-off with homework done. The client does the briefing on Tuesday, the meeting is on Thursday and we arrive read. The meeting ceases to serve to gather information and serves to decide. *Recurrent creative briefing, one session per campaign. * Qualification before the sales call. WHY IS GOHIGHLEVEL INTERESTED Agent Studio today looks inward. This opens up a new type of private channel between the platform and the end customer, just at the point in the cycle where more hours are lost and where a newly signed customer cools down faster. In addition, today's agencies do off-platform work: Zoom meetings, Google Meets, Notion, Shared Drive, email chains, chat. Customer data that ends up far from the CRM and then someone copies by hand. With the private link, the briefing goes directly to the contact and is available for workflows, custom fields and reports.
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Enable Managed AI Agents to Analyze Images, Videos, Audio & Documents
Problem Currently, GoHighLevel Managed AI Agents can generate content but lack the ability to properly analyze files sent by customers during conversations. When a customer sends an image, video, audio recording, or document attachment through SMS, WhatsApp, Live Chat, or other supported channels, the AI cannot interpret its contents and respond intelligently. This significantly limits the usefulness of AI agents in real customer interactions. Proposed Solution Introduce multimodal file understanding for Managed AI Agents, allowing them to automatically access, analyze, and understand customer attachments. Requested Capabilities 1. Image Understanding Analyze photos, screenshots, and graphics. Identify objects, products, and visual details. Extract text using OCR. Answer questions about images. 2. Video Understanding Analyze uploaded videos and their contents. Understand scenes, actions, and visual context. Transcribe and interpret spoken dialogue. Summarize videos and answer questions. 3. Document Understanding Read PDFs, Word, Excel, PowerPoint, and text files. Extract information from tables and forms. Summarize, compare, and answer document-related questions. Process scanned documents. 4. Audio Understanding Transcribe voice notes and audio files. Understand spoken requests. Identify relevant information and respond accordingly. 5. Automatic Attachment Processing Automatically detect incoming attachments. Analyze files without requiring manual intervention. Combine file contents with conversation history. Support multiple attachments in one message. Allow agents to use extracted information in workflows and CRM updates. Example Use Cases Hair Salon: Customer sends a hairstyle photo; AI identifies the style and recommends the appropriate service. Real Estate: Customer sends property photos; AI analyzes visible features. Education: Student uploads a transcript; AI extracts relevant academic information. Customer Support: Customer sends a screen recording; AI identifies the reported issue. Expected Outcome Managed AI Agents should not only generate images, videos, and documents but also understand files received from customers . This would enable truly multimodal, context-aware AI conversations and significantly reduce human intervention.
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Multi-Agent Collaboration, Delegation & AI Agent Management (like Grok bot, ChatGPT Dots and Meta Muse)
Enable Managed AI Agents to manage, supervise, delegate tasks to, and collaborate with other Managed AI Agents , similar to the multi-agent capabilities associated with platforms like Grok Bot. Instead of each AI agent operating independently, HighLevel should support entire AI teams with organizational hierarchies , where agents can coordinate, communicate, and execute complex projects together. Proposed Features Agent-to-Agent Communication Agents can communicate directly with other agents. Share information, files, context, and task progress. Request assistance from specialized agents. Collaborate on shared projects and objectives. Maintain shared conversations and team workspaces. Agent Hierarchies & Management Allow users to create organizational structures such as: CEO (Human) → AI Operations Manager → AI Team Leaders → Specialized AI Agents Manager agents should be able to: Assign and delegate tasks to subordinate agents. Coordinate multiple agents simultaneously. Monitor progress and performance. Review and validate completed work. Reassign tasks when agents encounter problems. Escalate issues requiring human intervention. Autonomous Team Execution Users should be able to assign a single objective to an AI manager. For example: "Launch our new product." The AI manager automatically: Creates a project plan and breaks it into tasks. Delegates research to the Research Agent. Assigns content creation to the Content Agent. Assigns landing page development to the Website Agent. Assigns campaigns to the Marketing Agent. Coordinates dependencies and reviews deliverables. Reports progress and results to the user. Agents should be capable of working simultaneously or sequentially , depending on task dependencies. Dynamic Agent Creation & Delegation Manager agents can recommend or create specialized subagents when necessary. Automatically select the most suitable agent for each task. Reuse existing agents and their skills. Support temporary agents for specific projects. Allow agents to request additional tools, skills, or permissions, subject to approval. Shared Memory & Knowledge Shared team knowledge bases and project context. Individual agent memory and specialization. Automatic handoffs between agents. Persistent project history and decisions. Permission-controlled access to shared resources. Collaborative Workspace in the GHL Mobile App Introduce a dedicated AI Teams section within the Managed Agents mobile experience. Users should be able to: View their AI organizational hierarchy. Chat with individual agents or entire teams. Assign objectives to AI managers. View agent-to-agent conversations and task assignments. Track projects, dependencies, and execution progress. Approve actions and resolve escalations. Receive notifications when agents complete tasks or need assistance. Governance & Human Oversight Configurable agent management permissions. Approval requirements for sensitive actions. Execution logs and agent accountability. Task budgets and resource limits. Ability to pause, stop, or override any agent. Protection against unauthorized delegation or infinite agent creation. Example AI Organization Human Business Owner AI Chief Operating Officer - AI Sales Manager - Lead Generation Agent - Appointment Booking Agent - AI Marketing Manager - Content Creation Agent - Advertising Agent - Social Media Agent - AI Customer Success Manager - Support Agent - Onboarding Agent The business owner gives instructions to the AI COO, which coordinates the other agents and reports results. Why This Matters HighLevel already offers Managed AI Agents, workflows, CRM data, and integrations. The next evolution should be enabling these agents to operate as coordinated AI organizations rather than isolated assistants . This would allow businesses to delegate entire business functions—not just individual tasks. The ultimate vision: One human manages an AI executive, the AI executive manages AI teams, and those teams collaboratively execute business operations inside HighLevel.
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Allow Managed AI Agents to Connect and Create MCP Integrations Through Prompts
Enable GoHighLevel Managed AI Agents to discover, connect, configure, and create Model Context Protocol (MCP) integrations simply through natural language prompts, similar to ChatGPT. Users should not need to manually configure MCP servers, APIs, or tool definitions. Proposed Solution Allow users to instruct Managed Agents to connect themselves to external applications or build custom MCP integrations. Example prompts: "Connect yourself to Google Drive using MCP." "Find and connect an MCP server for GitHub." "Connect to this MCP server: https://example.com/mcp ." "Read this API documentation and build a custom MCP integration." "Connect yourself to our inventory system using its API." Key Features 1. Prompt-Based MCP Connections Discover and connect compatible MCP servers. Automatically configure tools and permissions. Support OAuth and API key authentication. Test connections before activation. 2. Custom MCP Creation Generate MCP integrations from API documentation. Automatically create tool schemas and configurations. Deploy through supported hosting. Modify and troubleshoot integrations through prompts. 3. Agent-Initiated Discovery Agents should recognize missing capabilities and suggest integrations. Example: User: "Create a task in ClickUp." (I know the ClickUp mcp already exists, this is just an example for if it didn't exist) Agent: "I need ClickUp access. Would you like me to connect its MCP integration?" Once authorized, the agent connects and completes the task. 4. Centralized MCP Management Share connections across Managed Agents. Manage integrations from a central MCP hub. Control agent-level permissions. Enable or disable individual tools. 5. Security Secure credential storage. User approval for connections. Granular permissions and audit logs. Why This Matters Managed Agents should be able to expand their capabilities without requiring developers or complex manual configurations. The goal: If users can describe the capability they need, their Managed Agent should be able to discover, connect, or build the necessary MCP integration through conversation.
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