學習
運行生產級 AI 智慧代理的實用指南:執行階段架構、協調模式、框架選擇與代理威脅模型。
Agent Handoff Patterns: Routing Work Between AI Agents
Agent handoff patterns define how AI agents transfer tasks, context, and credentials: push handoff, pull dispatch, blackboard-pointer routing, and streaming transfer for multi-agent systems.
Agent2Agent Protocol — A2A Security, Architecture, and MCP Comparison
Agent2Agent (A2A) protocol lets AI agents delegate tasks across frameworks and vendors. Learn the architecture, security model, task lifecycle, and how A2A compares to MCP for multi-agent systems.
Agentic RAG: Sub-Question Decomposition, HyDE, and Corpus Security
How agentic RAG works: iterative retrieval loops, sub-question decomposition, HyDE, FLARE, and re-ranking. Covers multi-agent pipelines, corpus injection attacks, and embedding model selection.
Agentic Workflow - Patterns, Security, and Production Design
An agentic workflow is a multi-step AI process where agents autonomously choose tools, delegate subtasks, and adapt based on intermediate results. Core patterns, failure modes, and security design.
AI Agent Architecture — Layers, Topologies, and Security-First Design
AI agent architecture: four structural layers (perception, reasoning, memory, action), single vs multi-agent topologies, ReAct loop, and 2025 cross-agent standards (Google A2A, OpenAI handoffs).
AI Agent Context Window — Management, Limits, and Security
AI agent context window management: token budgets, context poisoning via OWASP LLM01:2025, compression strategies, and how OpenLegion enforces per-agent context isolation.
AI Agent Evaluation: Benchmarks, Metrics, and Testing
AI agent evaluation is the discipline of measuring whether agents reliably complete tasks, use tools safely, and stay within cost bounds — covering benchmarks, LLM-as-judge, and trace analysis.
Best AI Agent Frameworks (2026 Comparison)
Compare the best AI agent frameworks: OpenLegion, OpenClaw, LangGraph, CrewAI, AutoGen, Semantic Kernel. Side-by-side features, security, and pricing.
AI Agent MCP Security: Red-Team Guide to Exploit Prevention
Red-team guide to AI agent MCP security: tool poisoning exploits, rug-pull backdoors, supply chain attacks, cross-pipeline injection, and infrastructure hardening.
AI Agent Memory: Four Types, Security Risks, and Implementation
AI agent memory: in-context, vector store, structured K-V, and episodic types. Security risks from memory poisoning and credential exposure. OpenLegion vault-protected architecture.
AI Agent Observability — Tracing, Costs & Failure Modes
AI agent observability covers traces, costs, prompt versioning, and failure modes for autonomous agents in production. Why it differs from app observability — and what to track.
AI Agent Orchestration — Coordinate Agents
Container-isolated multi-agent runtime with fleet-model coordination (blackboard + pub/sub + handoff), credential vaulting, and per-agent budget controls.
AI Agent Platform — Deploy Secure Agents
OpenLegion is a managed AI agent platform with container isolation, credential vaulting, and budget controls. Bring your own LLM API keys.
AI Agent Sandboxing — Container Isolation, Escape CVEs, and Hardening
How AI agent sandboxing works: Docker, gVisor, WebAssembly, and seccomp hardening. Covers sandbox escape CVEs, code execution isolation, browser agent sandboxing, and runtime cost trade-offs.
AI Agent Security — Threats, Isolation, Vaults
AI agent security guide: credential leakage, prompt injection, sandbox escape, and how container isolation, vault-proxied credentials, and budget controls mitigate each threat.
AI Agent State Management — Checkpointing, Shared State, and Crash Recovery
Checkpointing, shared state, and crash recovery for AI agents. Covers LangGraph checkpointers, transactional blackboard writes, reducer patterns, snapshot isolation, and cross-agent synchronization.
AI Agent Tool Use — Function Calling, Schemas, and Safe Execution
How AI agents use tools: defining tool schemas, function calling across providers, parallel and chained calls, output parsing, error recovery, and permission scoping for safe production execution.
AI Coding Agents - Deploy Secure Dev Agent Teams
AI coding agents that plan, write, test, and review code autonomously. OpenLegion runs them in isolated containers with vaulted credentials and per-agent budgets - a self-hostable dev agent team.
Browser Use Agents — How AI Agents Control the Web
Browser use agents let AI autonomously navigate websites, fill forms, and extract data. Learn how they work, their security risks, and how to run them safely in isolated containers.
Claude Opus 4.8 - Capabilities, Cost, and Agentic Performance
Claude Opus 4.8 launched May 28 2026: 84% on Online-Mind2Web, fast mode 3x cheaper, dynamic workflows in Claude Code, first Opus to complete every Super-Agent benchmark case at GPT-5.5 cost parity.
Credential Management for AI Agents: Vault-Proxy Architecture
Vault-proxy architecture for AI agents: inject secrets server-side, isolate per agent, audit every access, and avoid the env-var anti-pattern that exposed keys in CVE-2024-34359 and CVE-2025-29927.
Grok 4: xAI's Frontier Model with Native Tool Use
Grok 4 is xAI's most capable reasoning model, released July 9 2025, trained on a 200,000-GPU cluster with RL at pretraining scale, native tool use, and API access via grok-4 model string.
LLM Cost Optimization — Six Levers for Production Agent Fleets
Six levers for LLM cost optimization: model routing, prompt caching, batch inference, context compression, per-agent budget caps, and output token control — with real cost numbers.
Managed AI Agent Hosting - Deploy Secure Agent Fleets
Managed AI agent hosting from OpenLegion: deploy container-isolated agent fleets on a dedicated VPS with vaulted credentials, per-agent budgets, and no infrastructure to run yourself.
Model Context Protocol (MCP) — How AI Agents Use Tools
Model Context Protocol (MCP) is Anthropic's open standard for letting AI agents discover and call external tools. How it works, security caveats, and OpenLegion's MCP support.
Multi-Agent Systems Architecture - Design, Topology, and Security
Multi-agent systems architecture defines how autonomous agents communicate, coordinate, and maintain trust boundaries. Covers topologies, inter-agent protocols, and production security design.
What Is an AI Agent? Definition and How They Work
What is an AI agent? A clear definition: an autonomous system that perceives, plans, and acts toward a goal using tools - plus how agents differ from chatbots and how the agent loop works.