Designing Agent Fallbacks and Graceful Degradation
Keep an agent safely useful when models, tools, data, or specialists fail—without fabricating success or silently weakening controls.
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Model Routing for AI Agents
Choose models by task requirements, policy, quality, latency, and cost instead of sending every step to one default.
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AI Agent Cost Optimization
Control the cost of successful agent outcomes, not merely the price of one model call.
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Agent Latency Explained
Why multi-step agents feel slow, where elapsed time accumulates, and how to improve speed without breaking the task.
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MCP Client Architecture
Understand how an MCP host manages dedicated clients, discovers server capabilities, applies policy, invokes operations, and handles failures.
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A2A Agent Discovery and Agent Cards
Learn how A2A clients discover remote agents, read Agent Cards, match skills and interfaces, evaluate suitability, and begin an interaction.
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Multi-Agent Coordination Patterns Explained
Learn seven practical multi-agent coordination patterns and how they manage roles, ownership, state, handoffs, aggregation, conflicts, and stopping.
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Centralized vs Decentralized Multi-Agent Architectures
Compare centralized, decentralized, and hybrid multi-agent architectures across control, state, coordination, scale, observability, governance, and resilience.
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Agent Routing Patterns: How Agents Choose the Next Worker
Learn seven agent-routing patterns, from deterministic rules and classifiers to semantic, capability-aware, hierarchical, and fallback routing.
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Orchestrator vs Supervisor vs Router in Multi-Agent Systems
Compare orchestrators, supervisor agents, and routers by purpose, decision ownership, state responsibility, delegation, routing, and workflow control.
