Latest Intelligence
AI Agent Intelligence
Practical guidance for understanding, building, architecting, and operating AI agent systems—from foundational concepts to production reliability.
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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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MCP + A2A Production Architecture
A production architecture for combining MCP capability access with A2A specialist delegation while preserving policy, identity, tracing, and failure boundaries.
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LLM Evaluation vs Agent Evaluation
LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.
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How to Evaluate an AI Agent
A practical workflow for defining agent success, building evaluation datasets, capturing traces, scoring behavior, analyzing failures, and preventing regressions.
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Building an MCP Server
A framework-neutral tutorial for designing, implementing, testing, securing, and deploying an MCP server over real backend systems.
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AI Agent Evaluation Explained
Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.
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Agent Traces and Trajectories
Learn how traces and trajectories represent observable agent execution without requiring storage or exposure of private chain-of-thought.
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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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Shared State and Context in Multi-Agent Systems
Learn how multi-agent systems separate local context from shared workflow state, exchange artifacts, synchronize updates, persist checkpoints, and avoid state conflicts.
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Sequential vs Parallel Agent Execution
Compare sequential, parallel, and hybrid agent execution by dependencies, latency, cost, state transfer, synchronization, aggregation, and failure handling.
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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.