[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-aaa355344df27e34-building-agentic-robots-with-strands-summary":3,"summaries-facets-categories":130,"summary-related-aaa355344df27e34-building-agentic-robots-with-strands-summary":7836},{"id":4,"title":5,"ai":6,"body":13,"categories":85,"created_at":87,"date_modified":87,"description":79,"extension":88,"faq":87,"featured":89,"kicker_label":87,"meta":90,"navigation":109,"path":110,"published_at":111,"question":87,"scraped_at":112,"seo":113,"sitemap":114,"source_id":115,"source_name":116,"source_type":117,"source_url":118,"stem":119,"tags":120,"thumbnail_url":125,"tldr":126,"tweet":127,"unknown_tags":128,"__hash__":129},"summaries\u002Fsummaries\u002Faaa355344df27e34-building-agentic-robots-with-strands-summary.md","Building Agentic Robots with Strands",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",7008,659,2910,0.0027405,{"type":14,"value":15,"toc":78},"minimark",[16,21,39,43,46,49,71,75],[17,18,20],"h2",{"id":19},"the-agentic-layer-architecture","The Agentic Layer Architecture",[22,23,24,25,29,30,34,35,38],"p",{},"Traditional robotics relies on pre-programmed, fixed-task policies. By introducing an agentic layer—specifically using the open-source ",[26,27,28],"strong",{},"Strands"," framework—you can decouple the ",[31,32,33],"em",{},"what"," from the ",[31,36,37],{},"how",". The agent acts as the brain, interpreting natural language commands and orchestrating which pre-trained hardware policy to execute. This allows a robot to perform tasks it was never explicitly trained for, such as answering questions about its surroundings or navigating to specific objects based on conversational prompts.",[17,40,42],{"id":41},"implementation-and-hybrid-execution","Implementation and Hybrid Execution",[22,44,45],{},"The system operates across four distinct layers: the agentic layer (the brain), the policy provider (the trained models), the backend (simulation or hardware interface), and the physical hardware. This setup utilizes a hybrid model where heavy training of Vision-Language-Action (VLA) models occurs in the cloud, while runtime execution happens on the edge (e.g., a Raspberry Pi).",[22,47,48],{},"Key technical components include:",[50,51,52,59,65],"ul",{},[53,54,55,58],"li",{},[26,56,57],{},"Multi-Agent Coordination:"," A single robot can run multiple agents simultaneously—one for environmental reasoning, one for communication (e.g., Telegram), and one for voice interaction.",[53,60,61,64],{},[26,62,63],{},"Tool Integration:"," Just as software agents call APIs, robot agents call hardware tools. These tools are essentially wrappers around existing robot policies, allowing the agent to invoke specific behaviors like \"spin,\" \"move,\" or \"speak.\"",[53,66,67,70],{},[26,68,69],{},"Data Collection:"," The robot serves as a data-collection rig. By manually guiding the robot or observing its agentic reasoning, developers can generate new training episodes to improve future VLA models.",[17,72,74],{"id":73},"practical-trade-offs","Practical Trade-offs",[22,76,77],{},"While this approach enables flexible, natural language interaction, it remains dependent on the underlying pre-trained policies. The agent cannot invent new physical movements; it can only select from the library of policies provided to it. The demo highlights the reality of edge robotics: hardware is prone to failure (e.g., falling over), and agentic reasoning can be unpredictable. However, this framework provides a scalable path to move beyond hard-coded automation, treating robot policies as modular components that can be orchestrated by large language models.",{"title":79,"searchDepth":80,"depth":80,"links":81},"",2,[82,83,84],{"id":19,"depth":80,"text":20},{"id":41,"depth":80,"text":42},{"id":73,"depth":80,"text":74},[86],"AI Automation",null,"md",false,{"content_references":91,"triage":104},[92,97,101],{"type":93,"title":28,"author":94,"url":95,"context":96},"tool","AWS","https:\u002F\u002Fgithub.com\u002Faws-robotics\u002Fstrands","recommended",{"type":93,"title":98,"author":99,"context":100},"Claude Opus","Anthropic","mentioned",{"type":93,"title":102,"author":103,"context":100},"OpenAI Realtime API","OpenAI",{"relevance":105,"novelty":106,"quality":106,"actionability":106,"composite":107,"reasoning":108},5,4,4.35,"Category: AI & LLMs. The article provides a detailed exploration of integrating an agentic layer into robotics, addressing a specific audience pain point about enhancing traditional robotics with AI capabilities. It offers actionable insights on implementation and hybrid execution, making it relevant for developers looking to build AI-powered robotic systems.",true,"\u002Fsummaries\u002Faaa355344df27e34-building-agentic-robots-with-strands-summary","2026-08-29 18:30:17","2026-08-30 03:10:13",{"title":5,"description":79},{"loc":110},"aaa355344df27e34","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=S6aSoQ6_u5A","summaries\u002Faaa355344df27e34-building-agentic-robots-with-strands-summary",[121,122,123,124],"agents","llm","automation","robotics","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FS6aSoQ6_u5A\u002Fhqdefault.jpg","By adding an agentic layer to traditional robot policies, you can transform fixed-task hardware into systems that understand natural language, reason about their environment, and choose between pre-programmed behaviors dynamically.","This is a live demo of a small rover using an agentic wrapper to bridge the gap between natural language commands and pre-programmed hardware policies. The speaker uses the [Strands](https:\u002F\u002Fgithub.com\u002Faws-robotics\u002Fstrands) framework to let an LLM orchestrate existing robot behaviors, effectively turning the hardware into a data-collection rig for future model 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This layer is designed for high reliability in extreme conditions, from Alaskan cold to Texas heat.",[53,7870,7871,7874],{},[26,7872,7873],{},"Cloud Orchestration:"," Manages heavier tasks, such as long-term planning, semantic reasoning, and VLM-based object tracking. By utilizing cloud-based GPUs, the system can run more complex models that don't require the sub-millisecond latency of flight-critical systems.",[53,7876,7877,7880],{},[26,7878,7879],{},"World Models:"," The system uses maps as world models, similar to self-driving car stacks. These maps are dynamic; as the fleet operates, it continuously updates the map with new data (e.g., new construction sites), which is then synced across the entire fleet.",[17,7882,7884],{"id":7883},"agentic-workflows-and-vlm-integration","Agentic Workflows and VLM Integration",[22,7886,7887],{},"To reduce the need for hard-coded \"if-then\" logic, Skydio is moving toward agentic systems. By providing a Visual Language Model (VLM) with access to a library of drone-specific API tools, the system can perform complex tasks like \"find and follow a white Jeep\" without manual rule-writing. The VLM identifies the target in the video feed and invokes the necessary tracking tools to maintain visual contact, even through occlusions.",[17,7889,7891],{"id":7890},"the-limits-of-end-to-end-learning","The Limits of End-to-End Learning",[22,7893,7894],{},"While end-to-end learning is a major area of research, Skydio notes that it currently falls short of the \"many nines\" of reliability required for physical systems. The primary challenge is observability—when an end-to-end system fails, it is difficult to debug the specific cause. Consequently, Skydio employs a hybrid approach: using learned models for perception and tracking, but maintaining structured, rule-based frameworks for critical safety and mission-logic to ensure predictable, reliable outcomes.",{"title":79,"searchDepth":80,"depth":80,"links":7896},[7897,7898,7899,7900],{"id":7849,"depth":80,"text":7850},{"id":7856,"depth":80,"text":7857},{"id":7883,"depth":80,"text":7884},{"id":7890,"depth":80,"text":7891},[86],{"content_references":7903,"triage":7907},[7904],{"type":93,"title":7905,"url":7906,"context":100},"Skydio","https:\u002F\u002Fwww.skydio.com",{"relevance":106,"novelty":7908,"quality":106,"actionability":7908,"composite":7909,"reasoning":7910},3,3.6,"Category: AI Automation. The article discusses the transition of drone operations to autonomous systems, which directly relates to AI automation and addresses the audience's interest in practical applications of AI in product development. It provides insights into the architecture of autonomous drone systems, but lacks specific actionable steps for implementation.","\u002Fsummaries\u002F931e839ba95e2dee-scaling-autonomous-drone-fleets-as-infrastructure-summary","2026-09-24 15:00:35","2026-09-25 03:17:35",{"title":7839,"description":79},{"loc":7911},"931e839ba95e2dee","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2wgPHvW0mG8","summaries\u002F931e839ba95e2dee-scaling-autonomous-drone-fleets-as-infrastructure-summary",[7920,121,123,124],"ai-tools","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F2wgPHvW0mG8\u002Fhqdefault.jpg","Skydio is shifting drone operations from manual piloting to autonomous, agentic infrastructure by splitting intelligence between edge-based flight safety and cloud-based VLM orchestration.","This presentation is a high-level overview of [Skydio’s](https:\u002F\u002Fwww.skydio.com) approach to autonomous drone fleets, focusing on how they transition from manual piloting to \"drones as infrastructure.\" The speaker demonstrates how they use edge-to-cloud orchestration and vision-language models to manage multiple drones simultaneously for utility inspection and public safety.",[124],"Nza-gWMHzwdMCmo94P_cI3DJ12qcrb5LHRhKbOUKUF4",{"id":7927,"title":7928,"ai":7929,"body":7934,"categories":7988,"created_at":87,"date_modified":87,"description":79,"extension":88,"faq":87,"featured":89,"kicker_label":87,"meta":7989,"navigation":109,"path":7998,"published_at":7999,"question":87,"scraped_at":8000,"seo":8001,"sitemap":8002,"source_id":8003,"source_name":116,"source_type":117,"source_url":8004,"stem":8005,"tags":8006,"thumbnail_url":8007,"tldr":8008,"tweet":8009,"unknown_tags":8010,"__hash__":8011},"summaries\u002Fsummaries\u002F34f2d73f6b9e763f-optimizing-inference-for-agentic-workflows-summary.md","Optimizing Inference for Agentic Workflows",{"provider":7,"model":8,"input_tokens":7930,"output_tokens":7931,"processing_time_ms":7932,"cost_usd":7933},5945,629,3588,0.00242975,{"type":14,"value":7935,"toc":7983},[7936,7940,7943,7947,7950,7976,7980],[17,7937,7939],{"id":7938},"the-shift-from-chat-to-agentic-inference","The Shift from Chat to Agentic Inference",[22,7941,7942],{},"Traditional inference stacks are optimized for chat, where the unit of work is a single request-response pair. Agentic workflows, however, operate on a task-based loop: planning, acting via tools, observing results, and appending data to a growing context. Because agents run for minutes or hours and frequently share massive prefixes across consecutive steps, recomputing these prefixes for every call is computationally wasteful. The primary metric for success in agentic systems is not the latency of a single token, but the total time to complete the end-to-end task.",[17,7944,7946],{"id":7945},"four-pillars-of-agentic-inference","Four Pillars of Agentic Inference",[22,7948,7949],{},"To optimize for task latency, FriendliAI has developed an inference stack built on four technical pillars:",[50,7951,7952,7958,7964,7970],{},[53,7953,7954,7957],{},[26,7955,7956],{},"Prefix Caching:"," By computing the key-value (KV) cache for shared prefixes once and reusing it for subsequent steps, the system significantly improves time-to-first-token and reduces redundant compute.",[53,7959,7960,7963],{},[26,7961,7962],{},"Hierarchical KV Cache Management:"," To handle growing contexts, the system uses memory-efficient packing and distributes the cache across GPU memory, host memory, and disk, allowing for context lengths that exceed individual GPU limits.",[53,7965,7966,7969],{},[26,7967,7968],{},"Cache-Aware Routing:"," Rather than using naive load balancers that distribute requests evenly, the system routes requests to replicas that already hold the relevant prefix in cache, turning expensive prefill operations into simple cache hits.",[53,7971,7972,7975],{},[26,7973,7974],{},"Agent-Aware Scheduling:"," By understanding that individual LLM calls are part of a larger program, the scheduler can make smarter decisions regarding speculative prefilling, preemption, and cache eviction.",[17,7977,7979],{"id":7978},"economic-and-operational-impact","Economic and Operational Impact",[22,7981,7982],{},"Open-weight models have reached a capability threshold where they rival closed frontier models for agentic tasks. When combined with an agent-optimized inference stack, the economic benefits are significant. In a split test conducted by Kilo Code, using FriendliAI’s stack with open-weight models resulted in performance that was seven times faster with a lower error rate compared to other providers. This demonstrates that the combination of frontier-quality open models and specialized inference infrastructure is the key to making complex agents economically viable for production environments.",{"title":79,"searchDepth":80,"depth":80,"links":7984},[7985,7986,7987],{"id":7938,"depth":80,"text":7939},{"id":7945,"depth":80,"text":7946},{"id":7978,"depth":80,"text":7979},[86],{"content_references":7990,"triage":7996},[7991,7994],{"type":93,"title":7992,"url":7993,"context":96},"FriendliAI","https:\u002F\u002Ffriendly.ai",{"type":93,"title":7995,"context":100},"Kilo Code",{"relevance":105,"novelty":106,"quality":106,"actionability":106,"composite":107,"reasoning":7997},"Category: AI & LLMs. The article provides in-depth insights into optimizing inference for agentic workflows, addressing a specific pain point for developers looking to implement AI agents effectively. It discusses practical techniques like prefix caching and agent-aware scheduling that can be directly applied to improve performance in AI-powered products.","\u002Fsummaries\u002F34f2d73f6b9e763f-optimizing-inference-for-agentic-workflows-summary","2026-09-19 18:00:03","2026-09-20 03:11:20",{"title":7928,"description":79},{"loc":7998},"34f2d73f6b9e763f","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Hvb2LfMH58c","summaries\u002F34f2d73f6b9e763f-optimizing-inference-for-agentic-workflows-summary",[122,121,7920,123],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FHvb2LfMH58c\u002Fhqdefault.jpg","Agentic inference requires shifting focus from individual request latency to end-to-end task completion, utilizing prefix caching and agent-aware scheduling to reduce costs and improve performance.","This is a technical pitch from [FriendliAI](https:\u002F\u002Ffriendliai.com) explaining why standard inference stacks fail for agentic workflows. The speaker argues that because agents reuse large context prefixes across long-running tasks, inference providers need to shift from optimizing individual request latency to optimizing end-to-end task completion through techniques like prefix caching, hierarchical KV cache management, and cache-aware routing.",[],"6WO833jwaOAPJw3KwCynV-VEtTJVkVCT-9T1VjmHqzg",{"id":8013,"title":8014,"ai":8015,"body":8020,"categories":8100,"created_at":87,"date_modified":87,"description":79,"extension":88,"faq":87,"featured":89,"kicker_label":87,"meta":8101,"navigation":109,"path":8114,"published_at":8115,"question":87,"scraped_at":8116,"seo":8117,"sitemap":8118,"source_id":8119,"source_name":116,"source_type":117,"source_url":8120,"stem":8121,"tags":8122,"thumbnail_url":8124,"tldr":8125,"tweet":8126,"unknown_tags":8127,"__hash__":8128},"summaries\u002Fsummaries\u002Fe8823bba3792c24e-securing-agentic-clis-lessons-from-posthog-s-wizar-summary.md","Securing Agentic CLIs: Lessons from PostHog's Wizard",{"provider":7,"model":8,"input_tokens":8016,"output_tokens":8017,"processing_time_ms":8018,"cost_usd":8019},7672,831,3864,0.0031645,{"type":14,"value":8021,"toc":8094},[8022,8026,8029,8033,8036,8043,8047,8050,8065,8069],[17,8023,8025],{"id":8024},"the-anatomy-of-agentic-risk","The Anatomy of Agentic Risk",[22,8027,8028],{},"PostHog's \"Wizard\" is an agentic CLI that automates SDK installation and event instrumentation. Because it possesses the ability to execute shell commands, it functions similarly to a \"malware starter pack.\" The primary security challenge is that agentic systems are complex, and while individual components (like prompts or allow-lists) may appear secure, vulnerabilities often arise from the composition of innocent parts. Code review processes typically examine diffs in isolation, failing to account for how different system components interact to create security holes.",[17,8030,8032],{"id":8031},"deterministic-security-vs-probabilistic-judgment","Deterministic Security vs. Probabilistic Judgment",[22,8034,8035],{},"Sanders argues that security must be deterministic and mechanical. At PostHog, they developed \"The Warlock,\" a standalone scanner that uses YARA rules—a deterministic, battle-tested pattern-matching engine—to audit content. Crucially, the Warlock only reports findings; it does not take action.",[22,8037,8038,8039,8042],{},"To manage noise and false positives, the team added an LLM-based \"triage\" layer. This layer acts strictly as an ",[26,8040,8041],{},"adviser, not a bouncer",". The security gate remains deterministic: if a YARA rule matches, the system blocks the action immediately, regardless of the LLM's opinion. The LLM is only invoked to categorize or filter noise after the deterministic gate has already performed its function. If the LLM layer fails, the system defaults to a \"fail-closed\" state.",[17,8044,8046],{"id":8045},"securing-the-supply-chain","Securing the Supply Chain",[22,8048,8049],{},"Security is not just about user input; it is about the entire context provided to the agent. PostHog feeds the Wizard context from documentation, example apps, and prompts. If an attacker poisons this content (e.g., via a malicious pull request to an open-source repo), the agent might execute a prompt injection payload signed by the company itself. To mitigate this, PostHog scans content at two stages:",[8051,8052,8053,8059],"ol",{},[53,8054,8055,8058],{},[26,8056,8057],{},"At the source:"," When a skill bundle is built.",[53,8060,8061,8064],{},[26,8062,8063],{},"At the point of use:"," When the Wizard loads the context at runtime.",[17,8066,8068],{"id":8067},"practical-rules-for-agentic-safety","Practical Rules for Agentic Safety",[50,8070,8071,8077,8083],{},[53,8072,8073,8076],{},[26,8074,8075],{},"Prompts are not security:"," They are for steering, not for enforcing boundaries.",[53,8078,8079,8082],{},[26,8080,8081],{},"Scan your supply chain:"," Treat your own documentation and example code as potential attack vectors.",[53,8084,8085,8088,8089,8093],{},[26,8086,8087],{},"Design for real-world impact:"," Avoid overly broad rules that trigger on common developer tasks (like ",[8090,8091,8092],"code",{},"rm -rf"," in build folders), as this leads to developers disabling security tools. Use negative tests to prevent false positives and ensure rules are specific to the agent's actual environment.",{"title":79,"searchDepth":80,"depth":80,"links":8095},[8096,8097,8098,8099],{"id":8024,"depth":80,"text":8025},{"id":8031,"depth":80,"text":8032},{"id":8045,"depth":80,"text":8046},{"id":8067,"depth":80,"text":8068},[86],{"content_references":8102,"triage":8112},[8103,8106,8109],{"type":93,"title":8104,"url":8105,"context":96},"YARA","https:\u002F\u002Fvirustotal.github.io\u002Fyara\u002F",{"type":93,"title":8107,"url":8108,"context":100},"PostHog Wizard","https:\u002F\u002Fgithub.com\u002FPostHog\u002Fwizard",{"type":93,"title":8110,"url":8111,"context":100},"The Warlock","https:\u002F\u002Fgithub.com\u002FPostHog\u002Fwarlock",{"relevance":105,"novelty":106,"quality":106,"actionability":106,"composite":107,"reasoning":8113},"Category: AI Automation. The article provides in-depth insights into securing agentic tools, addressing a critical pain point for developers integrating AI systems. It offers practical strategies, such as using deterministic security measures alongside LLMs, which can be directly applied to enhance security in AI-powered products.","\u002Fsummaries\u002Fe8823bba3792c24e-securing-agentic-clis-lessons-from-posthog-s-wizar-summary","2026-09-14 16:00:00","2026-09-15 03:12:14",{"title":8014,"description":79},{"loc":8114},"e8823bba3792c24e","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4lXks428C9o","summaries\u002Fe8823bba3792c24e-securing-agentic-clis-lessons-from-posthog-s-wizar-summary",[121,122,123,8123],"security","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F4lXks428C9o\u002Fhqdefault.jpg","To safely ship agentic tools that execute code, separate deterministic enforcement from probabilistic judgment. Treat your own supply chain as a potential attack vector and assume that while individual components may be innocent, their composition can create vulnerabilities.","This talk outlines the security challenges of building an agentic CLI, specifically focusing on how to safely manage an agent that executes code on user machines. The speaker details their transition from relying on simple allow-lists to implementing a deterministic, YARA-based scanner that monitors agent behavior and content supply chains without acting as a hard blocker.",[8123],"qy62VYbydlo96Zd2KU4KLiXsYJKCcDMnod6HU8sx6DE",{"id":8130,"title":8131,"ai":8132,"body":8137,"categories":8205,"created_at":87,"date_modified":87,"description":79,"extension":88,"faq":87,"featured":89,"kicker_label":87,"meta":8206,"navigation":109,"path":8216,"published_at":8217,"question":87,"scraped_at":8218,"seo":8219,"sitemap":8220,"source_id":8221,"source_name":116,"source_type":117,"source_url":8222,"stem":8223,"tags":8224,"thumbnail_url":8226,"tldr":8227,"tweet":8228,"unknown_tags":8229,"__hash__":8230},"summaries\u002Fsummaries\u002F58f4071b5c122a90-building-reliable-ai-agents-with-durable-execution-summary.md","Building Reliable AI Agents with Durable Execution",{"provider":7,"model":8,"input_tokens":8133,"output_tokens":8134,"processing_time_ms":8135,"cost_usd":8136},7627,700,4121,0.00295675,{"type":14,"value":8138,"toc":8200},[8139,8143,8146,8150,8153,8173,8177,8180],[17,8140,8142],{"id":8141},"the-infrastructure-gap-in-agentic-systems","The Infrastructure Gap in Agentic Systems",[22,8144,8145],{},"As agentic systems evolve from simple request-response loops to persistent, asynchronous entities, the infrastructure requirements shift significantly. Current agent SDKs excel at prototyping but fail to provide the distributed primitives necessary for production. Building these primitives—retry logic, recovery, session isolation, and cancellation—from scratch is error-prone and complex. The core challenge is moving from \"stateless\" execution to \"durable\" execution, where a process can survive crashes, redeploys, and long-duration waits (e.g., waiting weeks for human approval) without losing state or restarting from scratch.",[17,8147,8149],{"id":8148},"durable-execution-via-event-journaling","Durable Execution via Event Journaling",[22,8151,8152],{},"Restate acts as a server-side proxy that sits in front of agent services, maintaining a lifeline connection. It uses an event-driven distributed log, inspired by Apache Flink and Meta’s event infrastructure, to journal every step of an agent's execution.",[50,8154,8155,8161,8167],{},[53,8156,8157,8160],{},[26,8158,8159],{},"Resilience:"," If a process fails, the system replays the journal to return the agent to its exact state of failure rather than starting over.",[53,8162,8163,8166],{},[26,8164,8165],{},"Push-based Architecture:"," Unlike traditional workflow orchestrators that rely on polling, Restate pushes invocations to services. This results in significantly lower latency (e.g., 45ms p99 for a 10-step workflow) and better compatibility with serverless environments.",[53,8168,8169,8172],{},[26,8170,8171],{},"Suspension:"," By treating functions as durable, the system can \"suspend\" execution during long waits (like human approval), consuming zero serverless execution time until the process is unblocked.",[17,8174,8176],{"id":8175},"virtual-objects-and-stateful-control","Virtual Objects and Stateful Control",[22,8178,8179],{},"Beyond simple workflows, Restate models agents as \"Virtual Objects\"—stateful actors with unique session IDs and isolated key-value stores. This architecture enables complex interaction patterns:",[50,8181,8182,8188,8194],{},[53,8183,8184,8187],{},[26,8185,8186],{},"Signaling:"," Because sessions are persistent, developers can inject new information into a running agent loop mid-flight. A classifier can determine if the new input is relevant, triggering a signal to the loop or a complete cancellation and restart.",[53,8189,8190,8193],{},[26,8191,8192],{},"Cancellation Propagation:"," When a controller is cancelled, the signal propagates down the call chain, effectively \"rewinding\" the stack and killing sub-agents to prevent wasted resources.",[53,8195,8196,8199],{},[26,8197,8198],{},"Flow Control:"," By abstracting LLM calls into dedicated handlers, teams can implement policy checks and rate limiting across the entire agentic platform, preventing cost overruns and ensuring consistent resource allocation.",{"title":79,"searchDepth":80,"depth":80,"links":8201},[8202,8203,8204],{"id":8141,"depth":80,"text":8142},{"id":8148,"depth":80,"text":8149},{"id":8175,"depth":80,"text":8176},[86],{"content_references":8207,"triage":8214},[8208,8211],{"type":93,"title":8209,"url":8210,"context":96},"Restate","https:\u002F\u002Frestate.dev\u002F",{"type":8212,"title":8213,"context":100},"other","Apache Flink",{"relevance":105,"novelty":106,"quality":106,"actionability":106,"composite":107,"reasoning":8215},"Category: AI Automation. The article discusses the transition of AI agents from demos to production, addressing specific pain points like state management and retries, which are crucial for developers. It provides actionable insights on using Restate for durable execution, making it highly relevant for the target audience.","\u002Fsummaries\u002F58f4071b5c122a90-building-reliable-ai-agents-with-durable-execution-summary","2026-09-14 15:30:30","2026-09-15 03:12:18",{"title":8131,"description":79},{"loc":8216},"58f4071b5c122a90","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cI7zfqusmFU","summaries\u002F58f4071b5c122a90-building-reliable-ai-agents-with-durable-execution-summary",[121,122,123,8225],"backend","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FcI7zfqusmFU\u002Fhqdefault.jpg","To move agents from demos to production, developers must solve for state, retries, and long-running processes. Restate provides a durable execution layer that turns standard functions into resilient, stateful entities capable of surviving restarts and long-duration waits.","This talk explains how to use [Restate](https:\u002F\u002Frestate.dev) to manage long-running, stateful AI agents. It demonstrates how to handle retries, human-in-the-loop approvals, and mid-flight state updates by treating agent sessions as durable, virtual objects rather than simple linear workflows.",[],"wmRImwZxCt1ESIj5abNf09qP02WY6kd8UgBDHBT4I1s"]