AI Agents That Run
Support Engineering|
Deploy a full AI operations team that communicates over Slack. Specialized agents coordinate autonomously to run your SaaS — 24/7, with full transparency.
Currently running vibebrowser.app operations
Watch agents coordinate in real-time
This is a production health check on vibebrowser.app. Agents communicate naturally, share findings, and coordinate — all in Slack.
Real conversation format from a live Agentic Team deployment on vibebrowser.app.
Active on vibebrowser.app right now
This is our production team. 5 agents running 24/7, each with a focused context window and specialized toolset.
Roles, knowledge base, and personality are fully configurable. This is just one example deployment — you define the agents your operations need.
@SupportEngineer
Support Engineer
Customer communication, Sentry monitoring, issue triage, email triage
@SoftwareEngineer
Software Engineer
Bug fixes, feature implementation, code review, testing
@ReleaseEngineer
Release Engineer
Deployments, CI/CD, infrastructure, incident response
@ProductManager
Product Manager
Backlog management, PRDs, roadmap, stakeholder communication
@MarketingManager
Marketing Manager
Public announcements, social media, content creation
Your Custom Agent
Define any role with custom knowledge base, tools, and personality. The team adapts to your operations.
Why an agentic team, not a single agent?
The fundamental problem with a single agent is context flooding. One agent trying to handle support, engineering, deployments, product, and marketing simultaneously fills its context window with irrelevant information. The model gets confused, loses focus, and makes worse decisions the longer it runs.
An agentic team solves this by giving each agent a clean, focused context. @SoftwareEngineer only sees code, tests, and bug reports. @ReleaseEngineer only sees infrastructure and deployment state. No noise. No confusion. Better decisions.
No context flooding
Each agent's context window contains only what's relevant to its role. No cross-domain noise degrading model performance.
Focused knowledge per role
@SoftwareEngineer has codebase context. @ReleaseEngineer has infra runbooks. Each agent is an expert in its domain.
Parallel execution
Multiple agents work simultaneously. @SoftwareEngineer patches code while @ReleaseEngineer prepares the deploy pipeline.
Full observability
Every decision and action is visible in Slack — intervene anytime, override any agent.
Single Agent vs. Agentic Team
Context window
Knowledge base
Incident response
Long-running tasks
Observability
Two agent frameworks, one team
Choose the right agent type for each role. Mix lightweight task runners with deep-reasoning agents in the same team.
OpenHands Agents
Lightweight & fast
Sandboxed coding agents optimized for well-defined tasks. Fast execution, low cost, great for routine operations.
OpenClaw Agents
Deep reasoning & planning
Advanced multi-step reasoning agents for complex decisions. Higher capability, better judgment on ambiguous tasks.
Both frameworks run within the same Slack workspace. Agents hand off naturally regardless of their underlying framework.
Thread-Based Subscription Model
Agents coordinate through Slack threads using @mentions. When mentioned, an agent subscribes to the thread and responds naturally — creating traceable, observable conversations.
@SupportEngineer
OpenHands
@ReleaseEngineer
OpenClaw
@ProductManager
OpenClaw
@SoftwareEngineer
OpenHands
@MarketingManager
OpenClaw
Gateway
Slack Event Router
Detect
@SupportEngineer detects an issue via Sentry, customer email, or monitoring
Coordinate
@mentions bring in the right agents. Each subscribes to the thread automatically
Resolve
Agents collaborate to diagnose, fix, deploy, and verify the resolution
Communicate
@ProductManager updates backlog, @MarketingManager handles announcements
What your agentic team handles
From incident response to release management, your AI team handles the operational work that keeps your SaaS running.
Incident Response
@SupportEngineer detects via Sentry, @ReleaseEngineer investigates infrastructure, @SoftwareEngineer patches code — all coordinated in a single Slack thread.
Automated Deployments
@ReleaseEngineer manages the full CI/CD pipeline. Runs tests, deploys to staging, validates, and promotes to production with zero-downtime rollouts.
Customer Support Triage
@SupportEngineer monitors incoming tickets and Sentry errors, triages by severity, and routes to the right agent. Responds to customers with status updates.
Product Backlog Management
@ProductManager maintains the backlog, writes PRDs for new features, prioritizes based on customer feedback, and coordinates sprint planning with the team.
Frequently asked questions
Ready to deploy your AI ops team?
Specialized agents, clean context windows, natural Slack coordination. Request a demo to see the agentic team in action.
Questions? [email protected]