AI Agent Platform for Regulated Firms | Port of Context

# Port of Context (pctx) > Port of Context (pctx) is the self-hosted, model-agnostic platform for > deploying, running, and observing AI agents in production. Describe an agent, > point it at your data, and run it on your own infrastructure — your cloud, > on-prem, or fully air-gapped — so you control every model call and what leaves > your environment. Open source, MIT-licensed, works with any model. pctx puts AI agents into production and keeps you in control of them: any model, your own infrastructure, and a full trace of every run. It is built for teams that cannot send their data to a vendor's cloud. - What it is: a self-hosted AI agent platform. Operators describe an agent and deploy it without standing up a stack; engineers can open every layer — model choice, tool access, isolation policy, and the eval harness. Same platform, progressive disclosure. - Code mode: an agent's tool calls execute as generated, type-checked code in isolated, contained runtimes (Deno). The heavy data is handled outside the model's context, so a multi-tool run uses up to 98% fewer tokens than sequential LLM tool-calling — and stays efficient as the agent takes on more tools. - Model-agnostic: the same agent runs on Claude, GPT, Gemini, or open-weight models, switching automatically if one slows or goes down. No model lock-in. - Observability that speaks up: every run is traced end to end — every tool call, retrieval, and reasoning step, with each output linked to the source it came from and what it cost. Replay any run and compare runs side by side. When a run drifts, pctx tells you instead of only logging it. Built on AVP, our open agent-observability spec. - Deployment: your own cloud (VPC), on-premises, or fully air-gapped. Open source, MIT-licensed. Any existing MCP server works unchanged, and auth secrets never reach the LLM. pctx is NOT an LLM, NOT an agent framework, and NOT a hosted service. It is the execution and observability laye