The toolbox behind modern AI agents.
Think beyond the model. Agents need tools, identities, data, execution environments, and controls to do useful work.
Coding
Connectivity
Data
Execution
Control
Tool research
What to read next
AI & Security
The New Developer Tooling Layer: Security for AI Agents
As coding agents gain access to repositories and external tools, security around permissions, provenance, and safe execution is becoming a new product category.
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
OpenAI Agents API in Public Beta: The Solo Founder Playbook for Managed Cloud Agents
OpenAI shipped the Agents API on September 10 with the Codex harness, hosted sandboxes, and multi-agent support — here is how an indie team should adopt it this week without rebuilding infrastructure.
AI
OAuth vs API Keys for AI Agents: Which Should Developers Use?
A practical comparison of API keys, OAuth, and service identities for AI agents.
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 Design: Why Narrow Tools Beat Generic API Clients
Giving an agent a generic HTTP client creates a huge capability surface. Narrow business tools make authorization, validation, and observability easier.
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 Server Design: How to Build Tools Agents Can Use Safely
An MCP server is a capability boundary. Good design keeps tools narrow, explicit, validated, and easy to audit.
AI
OpenAI vs Claude for Building AI Agents: What Developers Should Compare
Compare OpenAI and Claude on the runtime details that affect your workflow: tools, state, sandboxing, latency, cost, and evaluation.
AI
AI Agent External API Data Filtering: How to Limit What Agents Can See
An external API may return far more data than an agent needs.
AI
AI Agent Tool Schemas: How to Design Functions Models Can Use Reliably
A tool schema is an API contract between an agent and your application. Good schemas reduce ambiguity, invalid calls, and unsafe assumptions.
AI
AI Agent Idempotency: How to Make Tool Execution Safe to Repeat
An agent may retry the same action after a timeout even when the external system already completed it. Idempotency prevents duplicate side effects.
AI
AI Coding Agent Secret Protection: How to Stop Credential Leaks
Agents can read files, logs, environment variables, and command output where secrets may appear.
AI
AI Coding Agent Sandboxing: How to Isolate Agent-Generated Code
Coding agents can execute commands, install packages, inspect files, and run applications.
AI
AI Coding Agent Command Permissions: How to Control Shell Access
A coding agent with unrestricted shell access can do far more than edit source files.