Mental health is personal. Technology should make it easier, not harder. Grateful to work with teams who understand the difference. What's next? More workflows, better experiences, fewer barriers.

What we learned:. Good AI doesn't replace empathy. It removes the barriers to finding it. When someone needs help, every click matters. Every smooth interaction could save someone's day. #mentalhealthsupport.

6 months later: .→ 16,000 conversations automated .→ Real people connected with real help .→ Therapists spending less time on paperwork, more time helping. The boring stuff? Automated. The human quotient? Enhanced. #workflowautomation.

We built 2 AI Agents:. Agent 1: For people seeking help .→ Smart therapist matching .→ Guided rating system. Agent 2: For therapists .→ Streamlined verification .→ Automated listing process. #AI #Automation.

Finding a therapist shouldn't feel like solving a puzzle. But here's the reality:. -1 in 5 adults need mental health support.-Most give up after 2-3 failed searches.-Rating systems are broken or non-existent. asked us to fix this. Here's what we built

The real magic? Your Agent can now:. -Pull customer data from HubSpot.-Create a report in Google Sheets.-Share it in Slack.-Schedule a follow-up meeting. All in seconds!.

In Tars right now, you can connect your Agent to over 170 tools!. All in one conversation. No custom code!. #Workflow

Here’s how it works:. -Host: Orchestrates the entire system .-Clients: Establish 1:1 connections between hosts and servers .-Servers: Deliver focused functionalities through utilities, assets, and instructions .-Base Protocol: Defines the communication for all system components.

Then Anthropic introduced MCP. Now your Agent speaks one language (MCP) and can talk to any service that supports it. Think of it as the USB-C of AI integrations. One connection standard for everything. #AGENTIC.

Before MCP, each integration meant:. -Learning new API documentation.-Writing custom authentication flows.-Handling different data formats.-Building error handling.-Maintaining all this code separately. A simple "connect to 3 services" project became a month-long nightmare. #AI.

You've probably seen "MCP" everywhere lately and thought, "great, another tech acronym to learn.". But this one's actually worth your time. Model Context Protocol is fixing one of AI's biggest headaches. #MCP #tool #AI

Finally, add examples. AI learns better from samples than from explanations. Add examples that show what a good and accurate response could look like in the conversation flow.

Next is defining conversational flow. Structuring natural, logical conversations that your Agents can follow. For instance:. Stage 1: Gather info .Stage 2: Confirm details .Stage 3: Generate output. #AgenticAI #AgenticAutomation.

Next comes parameters. Parameters are adding the right controls and guardrails to guide AI’s behavior. It is like defining the boundary within which the AI Agent will function to prevent the responses from going haywire. #prompt #PromptEngineering.

First, define the role of the AI Agent in detail. Define the exact role of the AI Agent in detail to get a crisp and accurate response every time. #AI #agentsguide #Automation

After building hundreds of AI Agents, we found a simple prompting framework that works every time. These fundamentals are what separate Agents that deliver inconsistent, basic results from those that perform reliably and generate personalized outcomes. #PromptEngineering

The lesson?. AI Agents work when they solve specific, measurable problems. Focus on persistent problems and build one workflow at a time to tackle one problem, and you’ll see ROI.

Post-implementation, @CLCreditUnion experienced:. 1. Call volume reduction by 12%. 2. Contact form reduction by 20% for repetitive questions. 3. Increased engagement in handling loan, savings, and mortgage queries. #AgenticAI #Automation.

Our team created Doogle, a personalized AI Agent deployed on their website to:. ✅ Automate common queries .✅ Guide users through product recommendations ✅ Handle loans, savings, and mortgage questions. Just focused automation where it matters most. #CustomerFeedback

Last September, @CLCreditUnion (Croí Laighean Credit Union) had a problem:. 1. 34,000+ calls annually.2. Same questions on repeat.3. Team was drowning in routine queries. #CustomerService.