Rohan Mehta

Are you trying to get AI agents to work for you but the results aren’t hitting the sweet spot? Most AI systems fail not because they aren't powerful enough but because they don’t know when to ask for help. Building great workflows isn’t about “automating everything.” It’s about knowing what NOT to automate. At Inverter Network, we’re building systems that blend AI, automation, and human expertise to create workflows that actually work. Our resident AI expert @RowRowRoUrBoat has a framework for structuring multistep reasoning workflows that actually work so your automation isn’t just smart, but practical and reliable. 1. The Real Challenge of Automation Everyone talks about AI replacing human tasks, but the real challenge? Context. Some tasks are easy to automate (data entry, API calls). Others need human intuition (strategy, creativity). The sweet spot is a structured system where: ✅ Fully automatable steps run without intervention 🤖 AI handles complex reasoning 🧠 Humans step in only when absolutely necessary This isn’t just automation. It is orchestration. 2. Breaking Down the Workflow Not all workflows are equal. Depending on complexity, you can structure them as: 📌 Single Agent Model – One AI interacts with tools (simple tasks, low dependencies) 📌 Collaborative Network – Multiple AI agents share context (distributed intelligence) 📌 Supervisor Model – One AI oversees and coordinates other AI agents (structured delegation) 📌 Hierarchical Systems – Layered automation, each handling different levels of complexity No one-size-fits-all approach. The key is mapping dependencies and ensuring AI knows its limits. At Inverter, we’re using these models to build systems where AI doesn’t just run,it knows when to escalate decisions. 3. The Hidden Flaws in AI Automation Most “AI-driven” systems break because they: ❌ Assume the world is static (but real-world data is messy) ❌ Automate too aggressively without fallback mechanisms ❌ Don’t recognize when a task should be handed off to a human The fix? Dynamic routing. Instead of brute-forcing AI through every problem, design workflows where tasks flow between code, AI, and humans dynamically. 4. Categorizing Tasks for Efficiency Every workflow step should be assigned to one of three categories: 💻 Code Node – Fully deterministic (e.g., API calls, data transformation) 🤖 AI Node – Context-dependent reasoning (e.g., summarization, anomaly detection) 👩‍💻 Human Node – Requires creativity, ethics, or judgment If too many Human Nodes exist → The system is broken. If too many AI Nodes exist → It’s prone to catastrophic failure. 5. The Most Underrated Lessons in Workflow Design 🔁 Feedback loops matter more than first-pass accuracy. Good automation isn’t “set and forget.” It’s iterative refinement: where AI constantly learns from past outputs. 🚦 Decision routing beats brute force AI. Instead of forcing AI to do everything, create workflows where different systems work together efficiently. Let AI decide when to pass a task to another system (or human). ⚡ The best AI is the one that knows its own limitations. A well-designed AI should say: “I don’t know, but I know who/what to ask next.” That’s Inverter’s approach to automation! AI that works with humans, not against them. 6. Where This Works Best (And Where We’re Applying This at Inverter) 💡 Automated Code Documentation – AI-assisted updates when code changes 📊 AI Dashboards – Automated data integration + AI-driven insights 📖 Internal Knowledge Systems – AI-curated wikis with human validation 🔬 Research Workflows – AI-assisted insight extraction, human oversight The common theme? AI doesn’t replace humans. It optimizes how humans and machines collaborate. 7. Final Thoughts The future of AI isn’t in replacing humans. It’s in building systems where AI knows when to ask for human help. Instead of blindly automating, structure workflows that respect both human intuition and machine efficiency. If this made you rethink AI automation, quote it with your thoughts? Let’s design better systems.

While we were in Bangkok for Devcon, something unexpected happened. A bunch of people approached us not just to chat, but to play a game we created. 🎲 What game, you ask? The Token Orchestrator Card Game. And here’s what it achieved in just minutes: 1) It sparked curiosity. People got hands-on experience building innovative token economy workflows for real-world use cases. Suddenly, tokenomics wasn’t just theoretical. It was something they could see, build, and refine. 🔥 2) It attracted potential partners and patrons. By playing the game, people instantly understood what kind of token infrastructure would benefit them the most. A unique, fun way to showcase our work instead of just talking about it. 3) It made us memorable. People walked away with something tangible: a game that challenged them, sparked curiosity, and ignited new ideas. This is what meaningful merch should do and here’s why investing in something like it is a good idea for your organization too . 👇

Economies are complex and dynamic systems - yet, we are tied to static mechanisms that fail to adapt. Inverter is unlocking a novel design space for architecting markets. Enter the rabbit hole 🕳️ inverter.network/blog/vision… Thank you @el33th4xor for the shoutout 🙏🫡