MCP Sampling Explained: Model Calls Requested by Servers
A practical explanation of MCP sampling, including message flow, model preferences, tool use, approvals, security, and current design status.
Topic
#human-in-the-loop
47 articles — updated daily
MCP Roots and Filesystem Boundaries Explained
A security-focused guide to MCP roots, file URI validation, workspace boundaries, user consent, symlinks, and modern alternatives.
MCP Logging and Completion Utilities
A practical reference to MCP logging notifications and completion suggestions, including current status, security, and implementation boundaries.
MCP Elicitation: Requesting User Input Safely
A practical guide to requesting missing user input during MCP operations without confusing conversation, consent, or credentials.
Why MCP Exists: The Integration Problem It Solves
Understand why AI integrations become difficult at scale and how MCP creates a reusable boundary between AI applications and external systems.
What Is MCP? A Practical Guide for AI Agent Builders
A practical, beginner-friendly guide to the Model Context Protocol: its host-client-server architecture, tools and resources, request flow, trade-offs, and security boundaries.
MCP vs Function Calling: What Actually Changes?
A practical comparison of MCP and model function calling, including their boundaries, request flow, portability, security, and combined architecture.
MCP vs APIs: What Changes and When to Use Each
Learn how MCP relates to REST APIs and SDKs, what each layer owns, and why most production MCP servers still call existing APIs.
How MCP Works: From User Request to Tool Result
Trace an MCP interaction from server discovery through model tool selection, host authorization, execution, and the final answer.
MCP Architecture: Hosts, Clients, Servers, Tools, and Resources
A component-by-component guide to MCP architecture, including host and client responsibilities, server primitives, transports, and trust boundaries.
MCP Tool Permissions and Least Privilege
A practical permission model for controlling which MCP tools users and agents can discover, call, and approve.
MCP Security Threat Model for Production Systems
A practical threat model for MCP hosts, clients, servers, tools, credentials, model context, and downstream systems.
MCP Prompt Injection, Tool Poisoning, and Data Exfiltration
A defensive guide to prompt injection, tool poisoning, confused-deputy risks, and data exfiltration in MCP systems.
Deploy and Observe a Remote MCP Server
A production deployment guide for remote MCP servers covering network boundaries, identity, scaling, observability, and rollback.
MCP Tool Design: Schemas, Descriptions, and Structured Outputs
Practical MCP tool-design guidance covering names, descriptions, JSON Schema, structured results, errors, permissions, and testing.
MCP Error Handling: Timeouts, Retries, and Cancellation
A reliability guide to MCP errors, deadlines, retries, cancellation, progress, idempotency, ambiguous writes, and observable recovery.
MCP Authentication and Authorization Explained
A production-focused guide to authenticating MCP clients and authorizing users, tools, resources, tenants, and downstream actions.
MCP Tools Explained: How AI Agents Take Action
A practical guide to MCP tools, including schemas, discovery, execution, approvals, errors, and safe production design.
MCP Transports: STDIO vs Streamable HTTP
Understand how MCP messages travel over STDIO and Streamable HTTP and how to choose the right transport for local and remote servers.
MCP Prompts Explained: Reusable Workflows for AI Applications
A practical guide to MCP prompts, including discovery, arguments, message content, user control, safety, and design patterns.
MCP Lifecycle and Capability Negotiation
A current guide to MCP version compatibility, capability discovery, request metadata, feature use, and the transition from older handshakes.
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.
Production Monitoring for AI Agents
Turn traces, metrics, logs, and evaluations into selected production signals, thresholds, dashboards, and actionable alerts.
Rate Limits and Backpressure in AI Agents
Control overload before immediate retries turn constrained models, tools, or workers into a failure storm.
Deploying AI Agents to Production
A practical path from a local agent prototype to a controlled, observable, and reversible production service.
Agent Stopping Conditions Explained
A practical, production-oriented explanation of agent stopping conditions, with examples, boundaries, trade-offs, and failure handling patterns.
Handling Tool Failures in AI Agents
A practical, production-oriented explanation of tool failure handling, with examples, boundaries, trade-offs, and failure handling patterns.
Retries, Timeouts, and Failure Recovery in AI Agents
A practical, production-oriented explanation of retries, timeouts, and failure recovery, with examples, boundaries, trade-offs, and failure handling patterns.
Reliable AI Agent Architecture
A practical, production-oriented explanation of reliable AI agent architecture, with examples, boundaries, trade-offs, and failure handling patterns.
Human-in-the-Loop for AI Agents
A practical, production-oriented explanation of human-in-the-loop control, with examples, boundaries, trade-offs, and failure handling patterns.
Tool Permissions and Least Privilege for AI Agents
A practical, production-oriented explanation of least-privilege tool permissions, with examples, boundaries, trade-offs, and failure handling patterns.
Prompt Injection in AI Agents
A practical, production-oriented explanation of prompt injection in tool-using agents, with examples, boundaries, trade-offs, and failure handling patterns.
AI Agent Security Explained
A practical, production-oriented explanation of the security model of an AI agent, with examples, boundaries, trade-offs, and failure handling patterns.
Observability for AI Agents
A production observability model for agent, model, retrieval, tool, sub-agent, and infrastructure signals—with privacy and redaction controls.
MCP Security and Permissions
A practical security model for MCP trust boundaries, authorization, least privilege, approvals, external content, backend credentials, and auditability.
MCP + A2A Production Architecture
A production architecture for combining MCP capability access with A2A specialist delegation while preserving policy, identity, tracing, and failure boundaries.
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.
AI Agent Evaluation Explained
Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.
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.
Centralized vs Decentralized Multi-Agent Architectures
Compare centralized, decentralized, and hybrid multi-agent architectures across control, state, coordination, scale, observability, governance, and resilience.
Agent Workflows and Orchestration Explained
Learn how agent workflows and orchestration coordinate steps, dependencies, branches, parallel work, retries, checkpoints, tools, agents, and humans.
Agent Graphs and State Machines Explained
Learn how agent graphs and state machines make nodes, edges, branches, loops, checkpoints, transitions, retries, and terminal outcomes explicit.
Reflection in AI Agents: How Agents Review, Correct, and Improve Their Work
Learn how AI agents use feedback, critique, and execution review to detect mistakes, revise their approach, and improve results without endless retry loops.
Tool Use in AI Agents: How Agents Act Beyond the Model
Learn how AI agents select tools, prepare arguments, execute functions and APIs, observe results, recover from errors, and stay within safe permission boundaries.
Anatomy of an AI Agent: The 9 Core Components
A beginner-friendly breakdown of the model, instructions, tools, memory, state, planning, feedback, guardrails, and execution loop inside an AI agent.
How AI Agents Work: The Complete Execution Loop
Follow the seven-stage execution loop that lets an AI agent reason, choose actions, use tools, learn from results, and keep working toward a goal.
What Is an AI Agent? A Practical Mental Model
A practical explanation of what makes an AI agent different from a chatbot or fixed workflow, and how the agent loop turns model reasoning into action.