Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/azure-samples/ai-gateway/agents-mdgit clone --depth 1 https://github.com/Azure-Samples/AI-GatewayWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01322 | $0.01322 |
| Opus 5 | $0.00661 | $0.00661 |
| Sonnet 5 | $0.00264 | $0.00264 |
| Haiku 4.5 | $0.00132 | $0.00132 |
Grade A, and why
AI-Gateway AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This document describes the key directories in the AI Gateway workspace for AI coding agents.
Overview
The AI Gateway is an experimental repository exploring the AI Gateway pattern through Azure API Management. It focuses on managing AI services APIs with security, reliability, performance, and cost controls. Labs use Jupyter notebooks with Python, Bicep templates, and Azure API Management policies.
Directory Structure
labs/
Contains hands-on experimental labs, each in its own subdirectory. Labs are structured as Jupyter notebooks with supporting Bicep infrastructure files and APIM policies.
Categories of labs include:
-
AI Agents & MCP:
model-context-protocol/,mcp-client-authorization/,mcp-a2a-agents/,mcp-from-api/,mcp-prm-oauth/,mcp-registry-apic/,openai-agents/,ai-agent-service/,realtime-mcp-agents/,gemini-mcp-agents/ -
Model Integration:
ai-foundry-sdk/,ai-foundry-deepseek/,ai-foundry-private-mcp/,gemini-models/,aws-bedrock/,slm-self-hosting/ -
Load Balancing & Routing:
backend-pool-load-balancing/,backend-pool-load-balancing-tf/,model-routing/ -
Security & Access Control:
access-controlling/,content-safety/,private-connectivity/,secure-responses-api/ -
Monitoring & Logging:
built-in-logging/,token-metrics-emitting/ -
Rate Limiting & Caching:
token-rate-limiting/,semantic-caching/ -
Specialized Features:
realtime-audio/,image-generation/,function-calling/,vector-searching/,message-storing/,session-awareness/ -
Operations:
finops-framework/,zero-to-production/ -
Lab structure pattern:
-
README.md- README file to describe lab following the standard lab structure. -
<lab-name>.ipynb- Main Jupyter notebook with step-by-step instructions -
clean-up-resources.ipynb- Jupyter notebooks used to removed resources when the lab is finished -
main.bicep- Azure infrastructure deployment template -
params.json- Temporary file generated automatically for the bicep deployment. This file will not be commited to the repo. -
*policy.xml- Azure API Management policy files -
pyproject.toml- Lab-specific Python dependencies (only present when the lab needs deps beyond the root environment). -
src/- Supporting source code (when applicable)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 118 lines · 1,322 tokens per session scan A 3e5c41e73068
AI-Gateway AGENTS.md is an instructions file published in the GitHub repository Azure-Samples/AI-Gateway (980 stars, last pushed 25d ago), licensed MIT. It adds 1,322 tokens to every session, about $0.0066 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
openai-cookbook AGENTS.md
Instructions for openai/openai-cookbook, covering repository guidelines, project structure & module organization, build, test, and development commands, coding style & naming conventions and testing guidelines.
skills copilot-instructions.md
Copilot instructions for microsoft/skills, covering copilot instructions for agent skills, project overview, ⚠️ fresh information first, core principles and 1. think before coding.
azure-search-openai-demo bicep.instructions.md
Infrastructure as Code with Bicep.
GPT-RAG copilot-instructions.md
Instructions for Azure/GPT-RAG, covering repository development and release instructions, branching strategy, default behavior, feature development workflow and branch creation.
azure-dev documentation.instructions.md
Instructions for Azure/azure-dev, covering documentation maintenance, documentation structure, when to update documentation, documentation placement guide and documentation standards.
GPT-RAG AGENTS.md
Instructions for Azure/GPT-RAG, covering gpt-rag agent operating contract, priority, what this repository is, repository boundaries and how to work.