Custom agent runtime
A purpose-built orchestrator and Anthropic tool loop coordinate model calls, tools and long-running workflows instead of relying on a single prompt-response endpoint.
Multi-model agent platform
An AI growth operating system, not a content wrapper.
LaunchPilot coordinates research, generation, review, scheduling and publishing across a large multi-model system. It combines a custom Anthropic tool loop with production integrations, scheduled jobs, fallbacks and observability so the product can keep operating when providers or external APIs fail.
Private product repository. Architecture facts are derived from the route tree, AI modules, migrations and production job definitions.
57
AI modules
86
API routes
15
scheduled jobs
21
database migrations
System anatomy
A purpose-built orchestrator and Anthropic tool loop coordinate model calls, tools and long-running workflows instead of relying on a single prompt-response endpoint.
Claude, OpenAI, Gemini and Grok are routed by task, with fallback paths that protect critical workflows from provider-specific failures.
Authenticated social integrations, scheduled publishing and background jobs turn generated output into a complete production workflow.
Observability, failure handling, OAuth, Stripe and persistent job state make agent behavior measurable and recoverable after deployment.
Engineering judgment
Seniority is not the number of technologies in a project. It is the ability to identify failure modes early and choose boundaries that keep the system understandable after launch.
Each provider is treated as a replaceable capability with explicit routing and fallback behavior, not as the application architecture itself.
Publishing and research workflows are represented as observable jobs so retries are controlled and failures are visible.
OAuth expiry, rate limits, partial success and provider downtime are designed into the workflow rather than handled as edge cases later.
Production stack