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All digests tagged State Management
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Arca's AI-native approach uses long-horizon agents to run a financial services business. The system manages complex, multi-day workflows by integrating signal systems, persistent memory, and shared spaces.
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Pi Durable revolutionizes agentic engineering by creating proactive, persistent agents. Learn how these durable agents manage state, automate incident response, and operate autonomously across multiple clients, moving development from reactive to proactive.
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Anders Swanson details how to build a real memory layer for AI agents, moving beyond context windows and scratch files. He outlines a system using multi-model databases, hybrid search, and governance controls for persistent, scalable recall.
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The Gemini Interactions API handles server-side state and long-running background jobs, replacing user/model turns with a structured timeline of steps. Agents run in a managed cloud sandbox, allowing them to execute code and share persistent environments.
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Learn how to build durable, multi-agent AI systems that handle human-in-the-loop processes. Discover why blocking calls fail and how Temporal's primitives ensure state persistence during long waits.
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The Gemini Interactions API and Managed Agents enable robust, stateful agentic workflows. Key features include persistent context via `interaction ID` and secure, remote sandboxes using `environment ID` for complex, multi-modal pipelines.
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Long-horizon agents require structured experiments, not just prompts. We introduce the Auto Research loop: a meta-system that runs controlled scenarios, scores behavior via a balanced scorecard, and proposes constrained policy changes to improve social consistency.
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Restate provides a durable foundation for building resilient agentic systems. It handles complex state management, recovery logic, and session isolation, allowing agents to survive crashes and long waits.
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Building Model-Composable Platform (MCP) apps requires a data-first approach. To integrate UIs into LLMs, developers must separate data processing from rendering and ensure all displayed information is passed back to the model's context.
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Discover how to build autonomous, multi-agent systems using LangGraph.js. Learn about orchestrating specialized agents (router, arbiter, committer) and managing persistent state in complex workflows.
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Agents are where microservices were in 2015. We discuss how to build reliable, stateful agents by focusing on skills as context units, using hooks for observability, and implementing guardrails at every tool call.
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AI agents have transitioned from model problems to distributed systems problems. Reliability requires incorporating concepts like idempotency keys, circuit breakers, and scoped credentials.
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Exo introduces a new paradigm: agents that achieve full recursive self-improvement by modifying their own code and policy at runtime. This architecture separates state, policy, and execution for safety and scalability.
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Memory harnesses solve context rot in long-horizon tasks by treating memory as a write-manage-read control loop, proving essential when answers fall outside the model's immediate context window.
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MCP Tasks enable durable, long-running asynchronous work for agents. The specification evolves from V1 (stateful) to V2 (stateless core), solving critical scaling issues inherent in distributed systems.
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This session guides developers from LLM theory to practical agentic implementations, detailing how primitives like structured output, tool calling, and guardrails build robust AI systems.
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Most agent failures are harness failures, not model failures. The system must own state transitions, enforce ordered commits, and use a run receipt to prove actions were visible to the user.
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Mastering the Agent Development Lifecycle requires moving beyond demos to systematic iteration across Build, Test, Deploy, Monitor, and Govern. Learn how advanced tooling uses tracing and online evals to ensure agents behave reliably at scale.
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Learn how LinkedIn built a hiring agent using LangGraph to cut time-to-interview by 60%. We cover the shift from static workflows to advanced plan-execute-replan loops and essential harness engineering techniques.
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