Guides for AI agents in production
How to let AI agents act on production systems safely: credentials, tool permissions, approvals, idempotency and stopping an agent. Each guide gives the answer first, then the code.
What is an execution layer for AI agents?
How an agent execution layer differs from authentication, authorization, orchestration and an LLM gateway, with Anlyon's supported paths, receipt grades and shared limits.
How AI agents share one refund limit, and why a new session does not reset it
Separate token spend, Anlyon usage budgets and shared impact limits. An impact limit counts money or any unit you declare, is reserved before dispatch, and keeps unknown exposure held.
AI agent tool execution: Anlyon, Arcade, Composio or build it yourself?
Choose by credential custody, approval enforcement, supported integrations and recovery. Includes framework HITL and AWS AgentCore Policy, with primary sources.
How to stop an AI agent from deleting your production database
Why AI agents delete production databases, and the boundaries that still hold when the agent decides to do it anyway: no production credential, separate environments, destructive operations as approved actions, isolated backups and a halt switch.
How to bind human approval to the action an AI agent executes
Prevent approval substitution: show the exact operation, bind its target and version, check freshness, and separate a decision from a verified outcome.
AI agent idempotency: how to stop duplicate charges, emails and records
Why AI agents repeat side effects (model re-calls, framework retries, restarts, timeouts after the write), how to choose an idempotency key per operation, and how to handle a call whose outcome is unknown.
LangGraph human approval without keeping the provider key in the graph
Where interrupt() fits, where credentials remain, and how to route a LangGraph tool through Anlyon's hosted action boundary.
AI agent secrets management: how to keep API keys away from the model
Where AI agents leak API keys (context window, tool arguments, environment, MCP config, logs), the four ways to hold a credential for an agent, and how to give an agent API access without it ever holding the key.
AI agent tool permissions: least privilege for every tool call
How to design tool permissions for AI agents in production: narrow tools instead of generic ones, validated inputs, fixed destinations, scoped keys, policies and approvals. What each layer stops, and what it does not.