Tool-calling loop
Model decides → tool executes → result returns → model continues until the task is complete.
Goal →Model →Tool →Result →Decision
← AI Agent Intelligence Architecture desk
Architecture patterns for agents that take real actions.
Start with the control boundary, then decide how the agent plans, calls tools, stores state, handles failure, and proves what happened.
Planner + executor
Separate planning from execution when a task needs multiple dependent actions or stronger controls.
Goal →Planner →Plan →Executor →Verifier
Human approval gate
Pause before high-impact actions such as sending money, deleting data, deploying code, or contacting customers.
Agent →Risk check →Approval →Tool →Audit
Sandboxed coding agent
Give coding agents an isolated workspace, scoped credentials, tests, and explicit promotion boundaries.
Task →Sandbox →Agent →Tests →Review
Read the research
Architecture-related articles
AI
MCP Client Architecture: How AI Agents Discover and Use Tools
MCP clients connect agent runtimes to external capabilities, but discovery does not equal authorization.
AI
AI Agent Tool Timeouts: How to Prevent Stuck Agent Workflows
External APIs hang, queues back up, and browsers stop responding. Agents need explicit timeout and cancellation behavior.
AI
AI Agent API Gateway Architecture for Production Workflows
A dedicated gateway can become the enforcement point between an agent and external services.
AI
MCP Tool Discovery: How to Keep Large Agent Toolsets Manageable
As an agent connects to more MCP servers, the available tool set can become difficult to reason about.
AI
AI Agent Latency Monitoring: How to Find Slow Agent Workflows
Agent latency is distributed across model calls, tools, queues, databases, and external APIs.
AI
AI Agent Cost Observability: How to Attribute Spending Per Workflow
Agent cost is difficult to control when model calls and external services are not tied to individual workflows.
Security
AI Agent Refresh Tokens: How to Handle Long-Running Workflows Securely
Long-running agents need access after short-lived access tokens expire, but permanent credentials create unnecessary risk.
Security
AI Agent Credential Rotation: How to Change Secrets Without Breaking Workflows
Credentials eventually need rotation, but rotating them carelessly can interrupt production agents.
AI
AI Agent Tool Calling Explained for Developers
Tool calling is the bridge between model reasoning and real application actions. The server still owns validation and authorization.
AI
AI Agent Architecture: Models, Tools, Memory, Permissions and Logs
A production agent is a software system around a model. Separate reasoning, tools, state, permissions, approvals, and observability.
Security
MCP Security for AI Agents: How to Review Tools Before Connecting Them
Connecting an agent to an MCP server expands its capabilities and its attack surface. Review tools, permissions, trust boundaries, and outputs before enabling them.
AI
MCP Prompt Injection Defense: How to Protect Agent Tools
MCP-connected content can contain instructions that attempt to influence an agent.