Calljmp

Calljmp is a managed agentic backend and AI orchestration platform for building production-grade AI agents and workflows as TypeScript code. Instead of stitching together prompts, queues, cron jobs, databases, and separate observability tools, Calljmp gives you a single runtime to define, run, and monitor multi-step AI systems end-to-end.

Developers write agents as composable functions and workflows in TypeScript, then deploy them to Calljmp’s hosted runtime with minimal setup. The runtime supports long-running executions, durable state, and pause/resume, so agents can handle real-world tasks that don’t fit into a single LLM call—like waiting for external tools, collecting more context, or branching based on user input. Calljmp also includes human-in-the-loop (HITL) checkpoints for approvals, escalations, and safe handoffs to a person when needed.

Calljmp is built for “tool-using” agents. You can expose your existing APIs, services, and databases as tools, connect knowledge sources for RAG and semantic retrieval, and orchestrate reliable LLM?tool loops with retries and guardrails. You’re not locked into a single model provider: since the logic is “agent-as-code,” you can swap LLMs, vector stores, or any layer of the stack as your needs evolve.

On the ops side, Calljmp ships with zero-config observability: traces, logs, errors, retries, latency, token usage, and cost tracking are captured automatically so teams can debug and improve workflows quickly. Typical use cases include embedded SaaS copilots, support agents for Slack/email, internal automation agents, competitive intelligence agents, and multi-step RAG pipelines that must be auditable and secure.

In short: Calljmp helps teams ship reliable, stateful AI agents without building orchestration infrastructure from scratch.