From manual operations to practical AI systems
Cheska’s work begins with the operational details most businesses quietly lose time to: repetitive follow-ups, data entry, customer questions, scheduling, reporting, and disconnected tools. That perspective keeps every build grounded in a real bottleneck instead of a technology demo.
Her work spans ClawBridge, ClawBridge Codex, OpenClaw and Hermes-Agent, TypeScript, Node.js, Docker, persistent memory, MCP integrations, CRM systems, outreach workflows, and production websites.
What Cheska means by an AI agent
An agent is not simply a chat interface. A production agent needs a bounded job, explicit tools, controlled access, observable execution, and a known path for retries or human escalation. The surrounding system matters as much as the model.
Positioning: AI Automation & AI Agent Engineer · Philippines · Available for remote builds worldwide.
How clients experience the work
Projects are organized around written scope, reviewable milestones, realistic tests, and an explicit handoff. Clients should understand what the system does, what it can access, where a human stays in control, and how the build will be operated after launch.
Public engineering evidence
ClawBridge Codex is the principal public example. It uses a Node host to orchestrate per-session agent containers, keeps credentials host-side, supports multiple communication channels, and integrates persistent memory. Its source, tradeoffs, and limitations are linked from the case study.