Practical frameworks, source-backed.

A hub for the Tokenmaxer AI Agent Control Gates cluster: monitoring, observability, tracing, security, MCP authentication, governance, evaluation, testing, human approval, and prompt caching.

A canonical Tokenmaxer guide to agent observability and AI agent observability with a trace schema, failure matrix, and internal-linking path into monitoring and security pages.

A practical agent testing plan for tool-using AI systems with scenarios for prompts, tools, retrieval, permissions, approvals, and budget stops.

A practical agent tracing schema for teams building tool-using AI agents, with fields for turns, tools, policy, cost, evals, and approvals.

A practical AI agent evaluation page with an eval gate template for task success, tool use, groundedness, policy compliance, and regression testing.

A practical governance framework for production AI agents, with owners, risk tiers, approval paths, eval gates, and audit requirements.

A practical monitoring checklist for production AI agents: runtime metrics, loop detection, tool-call errors, budget stops, cache regressions, and escalation rules.

A security threat model and checklist for production AI agents that call tools, touch private data, and trigger external actions.

A practical Meta ads operating model: decompose CPA into price, friction, value, and confidence, then use bubble charts to decide what to scale, fix, lower, or kill.

A human-approval matrix for AI agents that need to send messages, move money, deploy code, change data, or take customer-visible actions.

A bridge page for LLM observability searchers that explains where model telemetry ends and agent observability begins.

A practical argument for better embeddings: use LLMs to compile raw evidence into semantic cards, query probes, hard negatives, trust metadata, and eval harnesses.

An implementation-oriented MCP authentication guide for teams connecting agents to restricted servers and sensitive tools.

A practical MCP security checklist for teams connecting AI agents to tool servers, private resources, and external actions.

A support page for the agent-control cluster that explains prompt caching as a cost and context-layout gate for production agents.

A practical architecture guide for production agents: decompose the harness into replaceable jobs, design prompt caching into context layout, enforce tool policy in code, separate sessions from world state, and gate irreversible work.