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All digests tagged Agent Orchestration
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Runlayer secured 300+ agents by deploying a gateway to log every session and tool call. They found risks like prompt injection, data exfiltration, and cost blowups from model switches.
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Learn how to manage Claude Managed Agents in CI/CD using Ancho CLI and YAML. We cover multi-tenancy, the coordinator pattern, and scaling with self-hosted sandboxes.
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The future of AI products lies in 'always-on' agents that orchestrate themselves. We explore the shift from completions to multi-agent systems, demonstrating how agents can run complex, end-to-end businesses.
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Sydney Runkle details how to build a model router in the harness to reduce operational costs. By analyzing task traces and selecting models along the Pareto frontier, the router cut median cost per thread by 64%.
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Codex is demonstrated as an advanced AI agent capable of automating complex, multi-stage development workflows. Learn how to use Goals, Subagents, and the App Server to build 'software factories' and manage entire CI/CD pipelines.
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To ensure AI safety, agents must run in sandboxes that are modeled after the task's intent. Safety comes from limiting tools and context, not from the harness itself.
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Learn how to build custom HTTP channels for Managed Deep Agents to handle inbound requests from external services like HubSpot or iMessage. We cover defining verifiers, parsers, and managing user context.
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Charlie Holtz shares principles for fast building, advocating for 'orchestras, not factories.' Key strategies include staying near the frontier, creating slop-free zones, and giving agents free-range cloud sandboxes.
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AgentCraft is a game-inspired orchestrator that solves the human bottleneck of managing AI agents. It provides visibility, autonomy, and collaboration tools to scale agent use.
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Unmute is an open declarative standard for voice agents that solves vendor lock-in and prompt fragility. By compiling agent behavior into portable packages, it ensures structural guarantees and portability across platforms like LiveKit and PipeChat.
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AI agents are democratizing software creation, but failure can occur when product context is missing. The shift requires Product Managers to become 'agent orchestrators' to manage the new wave of non-developer builders.
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Learn about Harness Engineering: the architectural pattern for building scalable, production-grade AI agents. We discuss separating memory, tools, and runtime components for multi-tenant deployment.
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Master multi-agent coding with Herder. Learn how to set up persistent, customizable workspaces, orchestrate AI agents (Grock, Codeex, Claude), and integrate tools like zoxide and Lazygit into a single, powerful CLI environment.
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To build reliable AI agents, focus on Control Graphs—structured workflows (SOPs). Implement these using code frameworks like LangGraph or by defining SOPs in text for LLMs to follow. Always include verifiers and use scripts for deterministic steps.
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Instructions define an agent's behavior and can be managed via the Context Hub. This allows modifying instructions directly in the UI without redeploying code, streamlining agent maintenance.
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Learn how to build an autonomous social media agent using LangChain's Managed Deep Agents. We cover tool creation, scheduling, memory management, and full deployment to Slack.
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This episode analyzes Thinking Machines' Inkling (975B MoE), Meta’s cost-efficient Muse Spark 1.1, and Anthropic’s J-space paper, which reveals internal model processing for enhanced AI safety.
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Modal CTO Akshat Bubna details how AI workloads break traditional cloud assumptions. He discusses specialized primitives like GPU snapshotting, private IPv6, and DeFlash to enable elastic inference for custom models.
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