MCP Tool Permissions and Least Privilege
A practical permission model for controlling which MCP tools users and agents can discover, call, and approve.
Topic
#operate
31 articles — updated daily
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 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.
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.
Caching Strategies for AI Agent Systems
Reuse expensive results only when identity, freshness, authorization, and side-effect semantics make reuse safe.
Model Routing for AI Agents
Choose models by task requirements, policy, quality, latency, and cost instead of sending every step to one default.
Rate Limits and Backpressure in AI Agents
Control overload before immediate retries turn constrained models, tools, or workers into a failure storm.
Scaling AI Agent Systems
Grow workload capacity safely by separating stateless runtimes from durable tasks and protecting constrained dependencies.
Context and Token Cost Optimization
Build focused model context that preserves decision-relevant information while removing repeated and irrelevant tokens.
AI Agent Cost Optimization
Control the cost of successful agent outcomes, not merely the price of one model call.
Agent Latency Explained
Why multi-step agents feel slow, where elapsed time accumulates, and how to improve speed without breaking the task.
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.
Idempotency in Agent Workflows
A practical, production-oriented explanation of idempotency in agent workflows, 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.
Sandboxing AI Agents
A practical, production-oriented explanation of sandboxed agent execution, 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.
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.
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.
Agent Traces and Trajectories
Learn how traces and trajectories represent observable agent execution without requiring storage or exposure of private chain-of-thought.