How to move past the demo phase of agent building and onto shipping agentic products into production скачать в хорошем качестве

How to move past the demo phase of agent building and onto shipping agentic products into production

On November 10, 2025, at the GitHub office in San Francisco, Autonomy’s CTO and Founder, Mrinal Wadhwa, delivered a live, developer-focused walkthrough of how real products—not just demos—get built with agentic AI at scale. Speaking to an audience of engineers and product leaders, Wadhwa traced the evolution from a “state machine + LLM” prototype to production-grade multi-agent systems that demand tooling, orchestration, durable state, evals, cost control, and cloud runtime primitives. He then showed how Autonomy provides that full stack: a framework to write agents, the Autonomy Computer to run and scale them in the cloud, and a CLI to deploy, observe, and evaluate fleets. The session contrasted single-agent proof-of-concepts with the reality of multi-tenant, many-agent applications: agent-to-agent messaging, long-term memory and context management, tooling and MCP servers, distributed containers, and actor-model concurrency for massive parallelism. Wadhwa demonstrated a newsroom fact-checking app that spins up parallel “deep research” agents complete with a UI that was scaffolded in Next.js + shadcn/ui, and highlighted an enterprise loan decisioning use-case in which thousands of concurrent agents coordinate over a messaging fabric across connected containers—“map-reduce-style” distributed work without reinventing infra. He also showcased voice-based agents powered by streaming websockets that can handle thousands of simultaneous sessions for scenarios like first-round interviews. A recurring theme: productionizing agents requires more than a library like Langchain, Llama Index or CrewAI. Developers also need identity, messaging, persistence, isolation, context stores, fleet orchestration, and pragmatic cost/accuracy levers. Autonomy’s actor-style agents are ultra-lightweight; tens of thousands can run inside a modest container, communicating reliably across fleets of agents. With evaluator loops and scoped contexts, teams can use lower-cost LLMs where appropriate while maintaining quality and driving down spend. The outcome is a platform that helps teams move from “cool demo” to shipping product, aligning compute, concurrency, and observability with real-world SLAs. What viewers will learn in this video How to move beyond one-off agent demos to distributed, multi-agent applications that scale across users and workloads. Why actor-model concurrency enables high fan-out, message passing, retries, and long-running tasks. How context management (filesystems, scoped memory) and tooling via MCP enable deep-work agents that outlive a single prompt. How to deploy to Autonomy Computer, connect fleets of agents, and orchestrate those agents. Practical tactics to balance cost vs. accuracy with evals, small-context prompts, and cheaper models for specific subtasks. Why enterprises are shifting from “LLM features” to agentic products—and need a platform, not point solutions. About Autonomy Autonomy is a complete platform-as-a-service for agentic AI—combining a developer framework, cloud runtime (Autonomy Computer), and CLI—designed to build, secure, and scale distributed fleets of AI agents. Autonomy provides identity, end-to-end messaging, orchestration, memory, and tools so teams can run millions to billions of interoperable agents that coordinate complex workflows, connect to private data via Private Links, and power voice, research, and decisioning workloads. Learn more at https://autonomy.computer If you’re building agent-powered products—from research copilots and voice interviewers to decision engines—this talk shows how Autonomy turns prototypes into operational, scalable systems that teams can deploy, observe, and iterate quickly.