function-tools-approvals

A tutorial for connecting an AI agent to functions that require human approval before they run. It demonstrates a human-in-the-loop process, where the agent pauses, receives the user’s decision, and then continues.

In plain words
What is it for?
Use it to add approval steps to callable C# functions in a compatible Azure OpenAI chat-based agent. It explains how the caller passes the user’s approval or other required input back to the agent.
Why use it?
Some actions should not happen automatically, especially when they change data or have other consequences. Approval gives a person a chance to review a proposed function call first.

Agent for Codex

Install

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.

agentmods
npx agentmods add agents/managedcode/prompterone/function-tools-approvals
Clone the repo
git clone --depth 1 https://github.com/managedcode/PrompterOne

Made for: Codex.

Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,245 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00012 $0.02245
Opus 5 $0.00006 $0.01123
Sonnet 5 $0.00002 $0.00449
Haiku 4.5 $0.00001 $0.00225

Measured yesterday against content hash d789f72836eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

function-tools-approvals 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/tutorials/agents/function-tools-approvals.md · 244 lines

How it starts

The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Using function tools with human in the loop approvals

::: zone pivot="programming-language-csharp"

This tutorial step shows you how to use function tools that require human approval with an agent, where the agent is built on the Azure OpenAI Chat Completion service.

When agents require any user input, for example to approve a function call, this is referred to as a human-in-the-loop pattern. An agent run that requires user input, will complete with a response that indicates what input is required from the user, instead of completing with a final answer. The caller of the agent is then responsible for getting the required input from the user, and passing it back to the agent as part of a new agent run.

Prerequisites

For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.

Create the agent with function tools

When using functions, it's possible to indicate for each function, whether it requires human approval before being executed. This is done by wrapping the AIFunction instance in an ApprovalRequiredAIFunction instance.

Here is an example of a simple function tool that fakes getting the weather for a given location.

using System;
using System.ComponentModel;
using System.Linq;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI;

[Description("Get the weather for a given location.")]
static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

To create an AIFunction and then wrap it in an ApprovalRequiredAIFunction, you can do the following:

AIFunction weatherFunction = AIFunctionFactory.Create(GetWeather);
AIFunction approvalRequiredWeatherFunction = new ApprovalRequiredAIFunction(weatherFunction);

When creating the agent, you can now provide the approval requiring function tool to the agent, by passing a list of tools to the AsAIAgent method.

Read the full file on GitHub · 244 lines

Changes

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.

  1. yesterday First seen · 244 lines · 12 tokens per session scan A d789f72836eb

Subscribe to this mod's changes

function-tools-approvals is an agent published in the GitHub repository managedcode/PrompterOne (42 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 2,245 once invoked, about $0.0001 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.

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