The Orchestrator

Everybody is calling everything an agent: an editor window, a persistent runtime, a single function-call step, a model with a system prompt and a terminal. What people are trying to capture is agency: a system that uses inference, with the tools and framework needed to pursue an outcome. If that sounds abstract, that is the problem. The definition covers so much ground that every project, every company, and every vendor seems to be racing toward the delivery of “agentic everything.”

For this book, an agent is a system that uses an inference model to interpret a goal, choose actions, and use supplied tools and an operating framework to work toward an outcome. The model supplies the capacity to interpret and choose; the surrounding system supplies the context, tools, and limits within which it can act.

The clearest illustration is Cursor, because it has traveled the full distance. Cursor began in 2023 as an AI-assisted code editor — a VS Code fork with a chat window — and it still sells that editor. But as of this writing it is also a platform whose agents run in cloud-hosted sandboxes, work on their own schedules, produce merge-ready pull requests, and are reachable not only from the IDE but from a web interface, a command-line client, a phone and tablet app, Slack, Microsoft Teams, an SDK in TypeScript and Python, and scheduled automations — with specialized agent products for code review, security review, and pull-request approval layered on top.

One customer, the e-commerce company Faire, reported running more than two thousand automated agent runs a week.⁵⁴ The agent is no longer a feature inside the editor; the editor is one client of many for a fleet of agents that live in the cloud.

A personal note from the author. As one of Cursor’s early customers, I still find it odd to call Cursor an agent. To me it began as a simple extension of my coding environment: an editor that helped finish the line I was typing. But this is the way the industry is moving. Anything that touches AI seems to end up being called an agent eventually. I used Cursor heavily enough in its first year that the company sent me a USB Tab key — a single-key keyboard whose whole job was to accept an autocomplete suggestion. Today I spend more time verifying code Cursor wrote than writing code for it to finish. A Tab key would be a strange gift now, because there is no key for “do the whole task.” The name has widened as the work has changed.⁵⁵

Cursor’s competitors have traveled much of the same distance. Editor agents, cloud runs, and review paths are converging, although permissions, deployment options, integrations, pricing, and model access still differ. In August 2026, that last difference became open competitive pressure: after SpaceX acquired Cursor’s parent company, Anysphere, OpenAI announced that it intended to wind down model access for Cursor, with a proposed shutoff date of November 12, 2026. OpenAI said it could no longer be confident that the acquiring company would honor its terms; Cursor’s CEO replied that OpenAI models served only about five percent of its user traffic.⁵⁶ The incident shows a dependency beneath the interface: a model available through your chosen tool today may disappear because of a corporate relationship you do not control.

GitHub Copilot made the same move from autocomplete to coding agents. “Editor,” “chat assistant,” and “terminal tool” now describe the entry point more than the work. The contract tells you whether the system preserves state, calls other agents, has a permission envelope, or may touch production.

1.11.1 Persistent agent runtimes

Persistent agent runtimes keep an agent available across sessions, channels, schedules, and tasks by adding all the machinery around the model: a gateway, a workspace, memory, skills, scheduled jobs, authentication, profiles, and routing to tools or to other agents.

Two current examples make the category concrete. Hermes Agent, from Nous Research, combines persistent memory, reusable skills, tool access, MCP, scheduled automation, and isolated subagents across terminal, desktop, messaging, and remote execution surfaces. Nanobot, from HKUDS, is a lightweight self-hosted Python runtime with a WebUI, tools, long-term memory, MCP, model routing, multi-agent delegation, and scheduled automation.⁵⁷⁵⁸

Two open-source harnesses deserve particular attention, because they embody a philosophy rather than a feature set. Goose, from Block — the company behind Square and Cash App — is a local-first AI agent under Apache 2.0 licensing: the runtime runs on your machine, while inference may still use a hosted provider unless a local model — such as one served through Ollama — is selected. It pairs with a wide range of LLM providers and connects to tools through MCP — an early and deep adopter of the standard, with a growing catalog of listed extensions; it is also one of three founding projects of the Linux Foundation’s Agentic AI Foundation, the same organization that now governs MCP.⁵⁹ Pi, created by Mario Zechner of libGDX fame and now maintained by Earendil — the company of Armin Ronacher and Colin Daymond Hanna, which acquired the project in April 2026 — is a minimal terminal coding agent distributed under MIT license at pi.dev, and its design philosophy is the exact opposite of the integrated appliance: it ships a small core — loop, tools, context, sessions — and leaves sub-agents, plan mode, and permission gates to extensions, skills, and packages. By October 2026 it had added built-in MCP and Codemode for composing tool calls in a JavaScript sandbox, reached its 1.0 release, and kept the core small and adaptable. Zechner’s own framing of that philosophy is the shortest statement of participation I know: Pi isn’t a sealed product — if you need a command, tool, provider, or workflow tweak, ask Pi to build it, and it customizes itself in place. The harness is built to be changed by the person running it.⁶⁰ In October 2026 Earendil also shipped a second, experimental harness alongside Pi 1.0: Pi Durable, a framework for building agents whose conversations survive the process they run in — checkpointed tasks that resume after a crash, many conversations per harness with independent models and tools, forking, live extension replacement, and any number of clients watching and steering the same running agent.⁶¹ It is the same minimalism argument pointed at the problem Yegge walked away from: where Wheelhouse answered durability by going closed and bespoke, Pi Durable is the open bet that durable agent state can stay a swappable component. Goose and Pi are built to be owned, extended, and modified by the person running them. That gives the orchestrator an inspectable, adaptable harness and an exit path over a multi-year system life. A closed harness may be more convenient on day one. Either still needs identity, logging, retention, configuration control, and evidence generation; open code alone is not an audit trail.

The surrounding runtime is becoming a product category too. NVIDIA NemoClaw supplies managed agent sandboxes around runtimes including OpenClaw, Hermes, and Deep Agents. Its September 22 release adds configuration export and lifecycle controls through OpenShell. It belongs beside the harness as infrastructure for containing and operating it; an orchestrator still has to decide the policy that infrastructure enforces.⁶²

1.11.2 A note on personal and consumer agents

The 2026 agent releases extend well beyond engineering tools. A growing set of products lets a person hand over tasks through a conversation, connect their accounts, and return later for the result. This book focuses on designing and operating delegated systems: their contracts, permissions, evidence, and stops. Choosing and using a personal assistant is adjacent to that work, and does not receive the same treatment here. The products still belong on the reader’s map.

Representative announcements and expansions, checked October 8, 2026:

• Grok Bot. Launched in beta on August 11, with persistent agents that use a cloud computer and work across apps. It offers a consumer-accessible interface, but also targets teams and coordinated business tasks; calling it purely a consumer product would miss that scope.⁶³
• Muse. Meta introduced its personal agent on September 8, with a dedicated virtual machine, a browser, and interaction through the Muse app or WhatsApp. The advertised work includes personal tasks and longer-term goals.⁶⁴
• Gemini Spark. Google’s June 30 update brought its personal agent to the macOS app in beta, alongside connected apps and topic monitoring. It illustrates delegation spanning desktop files and online services.⁶⁵
• Perplexity Portable Computer. The local version of Perplexity’s multistep task agent expanded to compatible Windows RTX PCs on September 14. Its design includes local execution and optional use of cloud models, making the location of the work another choice to inspect.⁶⁶

The categories already overlap. Meta announced Muse for Small Business on September 29, adding business connectors and stating that publishing, sending, and spending require approval.⁶⁷ These are vendor descriptions, not independent demonstrations that every task succeeds or every boundary holds. Consumer-oriented does not mean inconsequential: an agent connected to email, a calendar, or a payment account has authority worth inspecting. The useful question is which parts of the delegation you can configure, enforce, reconstruct, and revoke, even when the vendor supplies the whole interface.