---
title: Reducing agent costs with AgentMon
description: See how AgentMon tracks AI agent costs, identifies spending on retries and idle sessions, and helps teams measure savings from specific fixes.
image: https://codenotary.com/hubfs/2026-10-05_19-23-59.png
---

**$ protect --distro linux --machines 25 --free**

[Start now](https://apps.codenotary.com/linux)

[![cn-logo-black-nobg](https://codenotary.com/hubfs/cn-logo-black-nobg.svg)](https://codenotary.com/)

- Product
  
  #### [![AgentMon Start](https://codenotary.com/hubfs/chub_backup/AgentMon%20Start.svg) **AgentMon Start** See what your AI agents are doing on your machine. TRY NOW →](https://codenotary.com/agentmon-start)
  
  #### [![AgentMon for Enterprise](https://codenotary.com/hubfs/AgentMon%20for%20Enterprise.svg) **AgentMon** Currently monitors more \> 7 million agent interactions/day. TRY NOW →](https://codenotary.com/agentmon)
  
  #### [![AgentX](https://codenotary.com/hubfs/AgentX.svg) **AgentX** Agentic network control middleware. TRY NOW →](https://codenotary.com/agent-network-control)
  
  #### [![Autonomous Security](https://codenotary.com/hubfs/Autonomous%20Security.svg) **Autonomous Security** AI Agents keep your servers secure. TRY NOW →](https://codenotary.com/trust)
- Use Cases
  
  #### [**AI Agent Risk Monitoring** Continuous oversight of autonomous agents across every environment.](https://codenotary.com/use-cases#risk)
  
  #### [**Autonomous Security Operations** Self-healing defenses that detect, contain, and remediate threats.](https://codenotary.com/use-cases#agentops)
  
  #### [**AI Coding Governance & Performance Monitoring** AI-generated code reviewed, tracked, and held to quality standards.](https://codenotary.com/use-cases#performance)
  
  #### [**AI Tool Cost & Usage Optimization** Spend and consumption optimized across every AI service in use.](https://codenotary.com/use-cases#cost)
  
  #### [**AI Tool Security & Policy Enforcement** Approved AI usage enforced with guardrails and policy controls.](https://codenotary.com/use-cases#security#security)
  
  #### [**Shadow AI Governance** Unsanctioned AI tools discovered, surfaced, and brought under control.](https://codenotary.com/use-cases#shadowit)
- [Blog](https://codenotary.com/blog)
- [Press](https://codenotary.com/press)
- Resources
  
  #### [**Integrations** Connect with your favorite tools and platforms. LEARN MORE →](https://codenotary.com/integrations)
  
  #### [**Support** Get help from our dedicated support team. GET HELP →](https://support.codenotary.com)
  
  #### [**Success Stories** Read how customers achieve their goals. READ MORE →](https://codenotary.com/success)
  
  #### [**Learn** Access documentation and learning resources. EXPLORE →](https://codenotary.com/learn)

[Login](https://apps.codenotary.com/auth/login)

[All posts](https://codenotary.com/blog/all)

 Oct 07, 2026

# Reducing agent costs with AgentMon

 By  [blog](https://codenotary.com/blog/author/blog)  ·   2 minute read

The Platform team spent $720 on agents in one week, against a budget of $650. AgentMon's cost view shows that $208 went to failed-call retries, abandoned sessions, and idle activity. That is 29% of the team's weekly spend.

Knowing where that money went makes the budget discussion more useful. [AgentMon](https://codenotary.com/agentmon)connects spending to session outcomes and proposes fixes with estimated savings and implementation effort. The examples in its weekly dashboards show how teams can decide what to change and check whether it worked.

## What the spending produced

Of Platform's $720 total, $372 went to sessions linked to a merged change within seven days. Exploration and planning accounted for $140. Failed-call retries cost $116, abandoned sessions cost $64, and idle sessions cost $28.

Exploration is recorded separately from retries and idle time. That matters because an investigation can be useful even if it produces no immediate code change. A team reviewing costs needs to understand what happened in the session before deciding whether to cut it.

AgentMon also shows cost per shipped change. Platform's figure is $10.59, down 6%, despite the increase in its total spending. Looking at delivery alongside the bill helps the team assess whether higher usage is producing more work.

![control agent spending](https://codenotary.com/hs-fs/hubfs/2026-10-05_19-23-59.png?width=1770&height=1125&name=2026-10-05_19-23-59.png)

## Why the bill increased

Platform spent $690 the previous week. AgentMon explains the $30 increase: additional Jira retries added $24, and more sessions per person added $18. A cheaper model mix saved $6, while better cache use saved another $6.

The connection problem consumed more than the savings from models and caching. Fixing Jira authentication is therefore a clear priority. The forecast shows the cost of leaving the current pattern in place: October spending of $3,050 against a $2,800 budget.

## Four proposed changes

The company view covers a broader scope than Platform's team budget. It shows total weekly spending of $1,880 and four decisions with estimated savings of $408 per week, approximately 22% of that total.

The largest proposal is to use service tokens and a three-retry limit for connected services. Estimated savings are $150 per week. The work requires one day of Platform time and a security review, with estimated payback in one week. Tokens would need rotation every 90 days.

A project memory rollout is estimated to save $130 per week. The proposal covers updating stale instructions, adding missing memory, and copying working setups to other projects. It requires ten engineering days in total and is estimated to pay back in about six weeks.

Making the standard model the default for small edits is estimated to save $88 per week. The proposed policy uses the largest model when a task changes more than 20 lines or when the user asks for it. The dashboard reports no measured risk in the Payments team's eight-week trial. This is a settings change with immediate estimated payback.

A two-hour idle timeout is estimated to save another $40 per week by limiting sessions left running overnight. Teams would need an opt-out for long builds and the ability to raise the limit where appropriate.

AgentMon presents each proposal with its cost, payback, and approval requirement. A manager can assess the engineering commitment and expected return before approving the work.

![2026-10-05\_19-21-42](https://codenotary.com/hs-fs/hubfs/2026-10-05_19-21-42.png?width=1761&height=1132&name=2026-10-05_19-21-42.png)

## Checking savings after the work is done

The optimization view reports $453 per week in possible savings and $87 per week realized so far. The $408 attached to the four pending decisions is a separate measure. These figures should not be added together or treated as money already saved.

Once a fix is implemented, its result needs to appear in subsequent data. Authentication changes should reduce retry spending. Better project memory should reduce repeated reading. Idle timeouts should lower the cost of sessions that continue after useful work has stopped.

AgentMon keeps the proposed savings and the realized results visible. The team can return to the same cost and session views after a change, assess its effect, and decide which remaining fix deserves attention next.

 

## Getting started

1. [Create your free Codenotary account](https://apps.codenotary.com/auth/signup?campaign=agentmon-start) and download AgentMon Start for Windows, macOS or Linux.
2. Install it and sign in with that account. The first sign-in becomes the admin.
3. Keep working. The collector picks up your agents' existing sessions, and the Overview fills in on its own.

Your first three devices are free for six months, and paid plans start at $15 per user per month.

[**Try AgentMon Start**](https://apps.codenotary.com/auth/signup?campaign=agentmon-start)

[![Share on twitter](https://4059529.fs1.hubspotusercontent-na1.net/hub/4059529/hubfs/01-marketplace/twitter-color.png?width=35&height=35&name=twitter-color.png)](https://twitter.com/intent/tweet?original_referer=https://codenotary.com/blog/reducing-agent-costs-with-agentmon&utm_medium=social&utm_source=twitter&url=https://codenotary.com/blog/reducing-agent-costs-with-agentmon&utm_medium=social&utm_source=twitter&source=tweetbutton&text=) [![Share on facebook](https://4059529.fs1.hubspotusercontent-na1.net/hub/4059529/hubfs/01-marketplace/facebook-color.png?width=35&height=35&name=facebook-color.png)](http://www.facebook.com/share.php?u=https://codenotary.com/blog/reducing-agent-costs-with-agentmon&utm_medium=social&utm_source=facebook) [![Share on linkedin](https://4059529.fs1.hubspotusercontent-na1.net/hub/4059529/hubfs/01-marketplace/linkedin-color.png?width=35&height=35&name=linkedin-color.png)](http://www.linkedin.com/shareArticle?mini=true&url=https://codenotary.com/blog/reducing-agent-costs-with-agentmon&utm_medium=social&utm_source=linkedin) [![Share on pinterest](https://4059529.fs1.hubspotusercontent-na1.net/hub/4059529/hubfs/pinterest.jpg?width=35&height=35&name=pinterest.jpg)](http://pinterest.com/pin/create/button/?url=https://codenotary.com/blog/reducing-agent-costs-with-agentmon&utm_medium=social&utm_source=pinterest&media=)

```json
{
  "@context" : "https://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "blog",
    "url" : "https://codenotary.com/blog/author/blog"
  },
  "dateModified" : "2026-10-07T12:00:00.104Z",
  "datePublished" : "2026-10-07T12:00:00.000Z",
  "headline" : "Reducing agent costs with AgentMon",
  "image" : [ "https://codenotary.com/hubfs/2026-10-05_19-23-59.png" ],
  "mainEntityOfPage" : {
    "@id" : "https://codenotary.com/blog/reducing-agent-costs-with-agentmon",
    "@type" : "WebPage"
  },
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "url" : "https://codenotary.com/hubfs/logo-light.svg"
    },
    "name" : "Codenotary, Inc."
  }
}
```

```json
{
  "@context" : "http://schema.org",
  "@type" : "Article",
  "author" : {
    "@type" : "Person",
    "name" : [ "blog" ],
    "url" : "https://codenotary.com/blog/author/blog"
  },
  "datePublished" : "2026-10-07T12:00:00+0000",
  "description" : "See how AgentMon tracks AI agent costs, identifies spending on retries and idle sessions, and helps teams measure savings from specific fixes.",
  "headline" : "Reducing agent costs with AgentMon",
  "image" : "https://23873599.fs1.hubspotusercontent-na1.net/hubfs/23873599/2026-10-05_19-23-59.png",
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "url" : "https://cdn2.hubspot.net/hubfs/23873599/logo-light.svg"
    },
    "name" : ""
  },
  "url" : "https://codenotary.com/blog/reducing-agent-costs-with-agentmon"
}
```