ElizaOps AI
Let Eliza handle your work, you build what matters
Demo
About
The problem it solves The Problem It Solves 1.** Startups and solo builders lose hours every day to operational tasks.** Writing emails, creating task boards, updating teammates, or even sending USDC payments — these actions eat up 30–50% of a builder’s time, reducing their focus on real product development. 2. **Existing AI tools don’t execute — they only reply.** Most AI agents today are limited to chat replies or summaries. But founders need agents that act, not just talk. There’s no bridge between natural language input and real task execution. 3. **Operational workflows are fragmented across too many tools.** Founders toggle between Gmail, Notion, Slack, and wallets just to complete a simple workflow. This leads to distraction, context switching, and dropped balls in communication or payments. 4. **Onchain tasks are still manual and error-prone.** Sending tokens, assigning DAO tasks, or handling payouts usually requires jumping into a wallet or a dApp. One wrong address or missed step can lead to irreversible loss or delays. 5. **There’s no assistant designed for Web3 productivity.** While tools like Slack GPT or Notion AI serve traditional teams, Web3 builders lack an agent that understands ops and onchain logic — especially one that can automate across both.
How it's made
Challenges we ran into 1. Building a seamless agent-to-execution bridge. One of the hardest challenges was designing the flow where natural language input triggers real operational outcomes — not just replies. Most LLMs are great at chatting, but turning agent decisions into tool executions (like sending emails or triggering smart contracts) required a full tool schema, dynamic routing layer, and careful execution tracing. 2. Managing real-time UI updates across multiple operational layers. ElizaOps runs as a multi-tab SPA (dashboard, task board, email panel, execution log). Keeping the agent output synchronized across those layers without causing React hook conflicts or performance drops was challenging. We implemented state-controlled rendering with Zustand and isolated reactive components to solve this. 3. Dummy tools that felt “too dummy.” In early iterations, tasks were just console logs. This broke immersion. We solved this by integrating Supabase for task storage, using realistic async delays in server actions, and adding visual feedback in the log panel to simulate execution more believably. 4. Role isolation in a single agent model. Supporting different agent roles (EmailBot, TaskBot, Web3OpsBot) with distinct tool access was tricky within a unified frontend. We solved this by injecting dynamic system prompts based on user-selected roles and rendering the allowed toolset accordingly. 5. Designing a user experience that feels like ops, not chat. Most agent UIs feel like chat rooms. We wanted ElizaOps to feel like an ops terminal. That required building custom components like LiveExecutionLog, TaskBoard, and EmailPreviewPanel from scratch — which added frontend complexity but delivered a much stronger UX.