Building the agent is only one piece of the puzzle. Once you start working with real businesses, suddenly there are projects to manage, client tasks to track, workflows to document, feedback to organize, and a million little things happening around the actual agent. That’s where my tool stack has been heading lately: - Claude / ChatGPT — designing workflows, debugging ideas, writing specs, and working through edge cases - Automation tools — connecting the agent to the systems the business already uses - Notion — SOPs, documentation, and reusable resources - Floment — keeping projects, tasks, progress updates, community, and AI assistance in one workspace I especially like having the execution layer separate from the actual agent logic. The agent can do its job, while the humans can see what needs to happen next. It sounds simple, but I think that becomes increasingly important once you move from “I built an AI agent” to “I’m operating AI systems for real businesses.” For those building managed AI agents, what are you currently using to keep the projects and client-side work organized?

Building an AI agent is one thing. Making it work smoothly with CRM, lead capture, follow-ups, calendars, and client workflows is where the real value comes in. I help businesses and agencies build that backend with AI + GHL + CRM automation. What’s the biggest challenge you’re facing with your AI agents?

Curious to see what everyone's end-to-end architecture looks like right now when deploying true agentic agents for clients. We’ve moved way past basic chatbots, and the infrastructure to build a fully managed AI employee has gotten deep. I’d love to know what your non-negotiables are across the entire stack. What are you using for: 1. The Brain & Logic: Are you relying primarily on Claude’s native computer use, testing Grok 4.5, or routing between multiple open-source models depending on the sub-task? 2. Compute & Harnesses: What is your core operating environment? (Orgo cloud setups, Hermes, OpenClaw, or custom desktop controls?) 3. Comms & Identity: How is your agent talking to the world? Are you hooking up AgentPhone for native voice/SMS/iMessage, using AgentMail, or building custom integration layers? 4. Security & Memory: How are you handling the tricky stuff? Where are you storing the agent's passwords and API keys securely (1Password, Doppler, custom vaults?), and what are you using to manage its memory and state without the stack turning into a debugging nightmare? The Client Reality: Tech aside, what specific workflow are you actually wrapping this stack around to charge that $5K/mo retainer? (e.g., outbound sales, automated data entry, full customer support reps?) Drop your full stack and your primary use case below. Let's compare notes!

> Trained myself to demo the agents properly. > Built a Second Brain with Fable 5 and Obsidian >Made a list of potential clients. Tomorrow it starts. Lead magnets go out. I've had enough days to sit with the fear and make peace with it. I'm excited!

How are you managing client deliverables, agent specs, and project tracking as you scale up your active managed agent retainers? Building and selling managed AI agents to businesses is great, but once I started running builds for multiple clients simultaneously, juggling client intake SOPs, agent specs, prompt libraries, and open browser tabs was completely killing my delivery momentum. I recently started testing a workspace called Floment (floment.ai) to keep project cards, task tracking, and built-in AI (Flowie) on a single screen. It lets me organize agent specs and client context right alongside native AI tools so I can stop app-hopping between Notion, ChatGPT, and separate task boards during builds. It’s helped clean up my fulfillment pipeline, but I'm curious how other agent builders structure this. What does your backend setup actually look like when handling multiple active agent clients? Are you keeping specs and tasks strictly in standard spreadsheets, or using a dedicated workspace board to keep builds moving smoothly?