#long-running Agent
299 related articles
·3 min
Enterprise Long-Running Agent Engineering: A Complete Guide from Demo to Production
A deep dive into enterprise Agent engineering: long-running execution, HITL safety approvals, and event sourcing — with two real-world commercial projects for content ops and SRE.
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·4 min
7 Core Components for Designing Long-Running AI Agents
A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.
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GPT-5.6 Trio Launches: Luna, Terra, an…
·3 min
GPT-5.6 Trio Launches: Luna, Terra, and Sol Built for Long-Running Agentic Tasks
OpenAI officially launches the GPT-5.6 family: Luna, Terra, and Sol, with 1M token context and a focus on long-running agentic performance. A deep dive into three-tier pricing, Agents' Last Exam results, the SWE-Bench Pro controversy, and new API features like programmatic tool calling and native multi-agent support.
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·5 min
Maximizing Codex Weekly Quota: A Complete Guide to Goal Commands and Long-Running Agent Configuration
Master Codex's Goal command mechanism. Use five standard project settings—agents.md, context.md, active-context and more—to solve context forgetting and hallucination in long-running Agents and maximize your weekly quota.
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·6 min
The Seven Core Components of Building Long-Running AI Agents
An in-depth look at the seven core components for building long-running AI agents: Goal, Evaluator, Verifier, Outer Loop, Orchestration, Observability, and Memory. Master this control system for reliable autonomous agents.
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·3 min
Deep Dive into the /goal Command: The Ultimate Tool for Managing Long-Running AI Agent Tasks
A deep dive into the /goal command in Claude Code and Codex — covering positioning, real-world cases, and a three-element Prompt framework (Goal, Termination Condition, Constraint Rules) for stable long-running AI Agent tasks.
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·1 min
Claude Fable 5 as a Computer Use Orchestrator Model: Excelling in Long-Running Agent Workflows
Anthropic launches Claude Fable 5 as a Computer Use orchestrator model for Pro and Max users, excelling in long-running agent workflows for automation tasks.
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·2 min
Root Causes of Long-Running AI Agent Failures and a Five-Layer Architecture for Control
Analyze the three root causes of long-running AI Agent failures — state loss, planning drift, and verification failure — with a five-layer architecture solution and six actionable engineering rules.
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Industry Insights·2 min
Five Design Patterns for Long-Running AI Agents: From Checkpoint Recovery to Cluster Orchestration
Deep dive into five core design patterns for long-running AI Agents from Google Cloud Next 26: checkpoint recovery, delegated approval, hierarchical memory, ambient processing, and cluster orchestration.
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·2 min
Capsomnia: An Open-Source Tool That Uses Caps Lock to Prevent Mac Sleep When Closed
Capsomnia is a free open-source Mac sleep-prevention tool that turns Caps Lock into a physical switch, keeping MacBook awake with lid closed for AI agents, builds, and SSH sessions.
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·3 min
OpenComputer: Deploy Managed AI Agents with a Single Sentence — A Minimalist Launch Solution for Developers
OpenComputer is an AI agent managed deployment platform that gives you a live agent URL from a single description. This article analyzes its workflow design, market positioning, and security/cost considerations.
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·1 min
Pushary: The Efficiency Tool That Lets You Approve AI Agent Requests with One Tap from Your Phone Lock Screen
Pushary pushes AI Agent confirmation requests to your phone lock screen, supporting Claude Code, Cursor, and Gemini CLI with QR pairing, per-tool policies, and audit trails for one-tap approvals.
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·4 min
Infrastructure Architecture for Agent Applications: Four Core Patterns Explained
A deep dive into infrastructure architecture patterns for production-grade Agent applications, covering state persistence, sandbox isolation, LLM observability, and cost control.
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·4 min
Infrastructure Architecture for Agent Applications: Four Core Patterns Explained
Deep dive into infrastructure architecture patterns for production-grade Agent applications, covering state persistence, sandbox isolation, LLM observability, and cost control.
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·4 min
Microsoft Open-Sources AI Agent Governance Toolkit: Comprehensive Coverage of OWASP Top 10 Risks
Microsoft open-sources agent-governance-toolkit covering all OWASP Agentic Top 10 risks through policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for production AI Agent deployment.
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·2 min
Three Major Updates to Gemini API Managed Agents: Free Tier, Cost Controls, and Scheduled Triggers
Google Gemini API Managed Agents launches three key updates: Free Tier for universal access, Cost Controls for budget safety, and Scheduled Triggers for automated execution.
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·3 min
Kimi K3 Launch: A 2.8 Trillion Parameter Flagship Model Makes Its Grand Entrance
Moonshot AI launches Kimi K3 with 2.8 trillion parameters and 1M token context. Google delays Gemini 3.5 Pro, AI coding tools upgrade collectively as competition shifts to coding and Agent capabilities.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to go from demo to production.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.
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·4 min
GPT-5.6 Value Maxing Practical Guide: From Token Stacking to Output Optimization Methodology
Deep dive into OpenAI GPT-5.6 Value Maxing strategies covering Sol/Terra/Luna model selection, KV cache optimization, Prompt compression, and programmatic tool calling to help developers achieve more output with fewer Tokens.
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