Agent Orchestration & On‑Device Inference

Orchard: An open framework for scalable agentic AI open-sources a Kubernetes-based environment, evaluation tooling, and training recipes for building agentic AI across domains. Outcome engineers get a reproducible platform for environment orchestration and evaluation that makes legible landscapes and deployment patterns easier to adopt (Principles 06, 07).

Launch HN: Hoplite (YC S26) – Effortlessly deploy cloud coding agents launches a service that simplifies deploying cloud coding agents, handling scale, security, and infra so teams can run developer agents without the ops burden. This lowers the operational cost of agent rollouts and turns agents into maintainable delivery lanes — a practical orchestration layer for agentic workflows (Principle 09).

Asana’s AI agents share memory across your company — but not your secrets describes Asana exposing a Work Graph as a shared, access-controlled memory for coachable AI teammates. Outcome engineers should study its memory partitioning and access-controls to scale agent teams while protecting sensitive context and ensuring auditability (Principles 03, 15).

AirLLM — 70B inference on a single 4GB GPU demonstrates streaming model layers and experts to run 70B-class LLMs on tiny GPUs by minimizing memory footprint. That technique reshapes deployment trade-offs for outcome engineers, enabling lower-cost, edge-capable agents and forcing new decisions around latency, batching, and state management (Principles 12, 04).

Cloudflare has mostly ditched third party security tools, suggests not trying that at home reports Cloudflare replacing many third-party security tools with autonomous agents to automate bug triage and incident workflows. This real-world migration shows how agents can supplant existing tooling at scale — study their model selection, CI/CD integrations, and rollback/gates to manage systemic and safety risk when agents run security-critical processes (Principles 09, 14).