Active
OpenClaw Setup
Our AI operations backboneInfrastructure
Overview
OpenClaw is an open-source multi-channel gateway for AI agents. We run it as the operational backbone of EIKO, connecting a single AI agent to Telegram, WhatsApp, webchat, and more. It handles everything from calendar checks to email triage to project management.
The Challenge
AI assistants are powerful in isolation, but they break down when you need them across contexts. You want to message from your phone, get a response in Telegram, continue the conversation on desktop, and have the agent remember everything. Most tools force you into one channel. We needed an agent that lives everywhere we do.
How It Works
Phase 1: Foundation
Define architecture and prerequisites up front: always-on host, API key management, and a clear channel strategy. The jump from chat tool to operating layer starts with a self-audit and a stable runtime baseline.
Phase 2: Identity & Access
Create user and profile isolation with explicit auth boundaries. Governance files and operational contracts keep behavior predictable and prevent drift when the assistant starts handling real workflows.
Phase 3: Channel Layer
Connect messaging surfaces including Telegram, WhatsApp, Discord, iMessage, and webchat so one agent can follow the same conversation context across devices and interfaces.
Phase 4: Runtime Deployment
Set up profile-specific deployment, ports, workspace separation, and auto-start so sessions survive restarts and remain reliable for always-on operations.
Phase 5: Configuration & Memory
Tune model tiers, skills, memory conventions, and guardrails. Durable memory with daily logs, curated long-term context, and corrections tracking turns one-off chats into compounding operational knowledge.
Phase 6: Pairing & Operations
Run first-time pairing flows, heartbeat routines, and cron-based automations that monitor email, calendar, and recurring tasks while only escalating when attention is actually needed.
Phase 7: Scale & Governance
Harden security, manage costs with model routing, and expand to multi-agent workflows. Autonomous sessions become reliable only after verification loops, deployment playbooks, and explicit guardrails are in place.
Tech Stack
Node.jsOpenClaw (open source)Claude (Opus for main session, Haiku for heartbeat, Codex for sub-agents)Multi-provider LLM routing
Lessons Learned
- Running AI in production is different from demos. Context window management, cost optimization across model tiers, and graceful failure handling are invisible in prototypes but critical at scale. Our setup uses three different model tiers for different task types.
- Memory is a design problem, not a storage problem. Raw conversation logs are useless for continuity. A curated memory layer (daily notes + long-term summaries + correction tracking) gives the agent genuine context without blowing up token costs.
- Multi-channel is about context, not connectors. The hard part is not sending messages to Telegram. It is maintaining coherent state when the same person talks to you from three different surfaces in the same hour.
Status & What's Next
Active. Running 24/7 for EIKO operations since January 2026.
Deeper automation workflows, client-facing agent instances, voice interaction improvements.
Want to deploy AI agents across your organization's communication channels? We've been running this in production.
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