Free Lesson

Building Monitoring for AI Systems (Pricing, Profits & ROI)

Hosted by Mahesh Yadav

Fri, Aug 14, 2026

4:00 PM UTC (45 minutes)

Virtual (Zoom)

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Agentic AI Product Management Certification using Claude Code
Mahesh Yadav
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What you'll learn

Calculate the True Cost of Every AI Agent You Run

Track every cost: API calls, infrastructure, escalation. Know exactly what your agents cost per request and monthly.

Price AI Services Based on Profit, Not Guesses

Learn pricing models (per-request, unit-value, tiered). Price services to cover costs, maintain margin, maximize profit.

Prove ROI With Data That Impresses Executives

Measure baseline and current ROI. Answer 'Is this profitable?' with data that clearly justifies continued investment.

Build Monitoring Dashboards That Drive Decisions

Create cost, profit, and ROI dashboards. Know when to optimize, scale, or shut down based on real business impact data.

Why this topic matters

AI agents are expensive. Most companies have zero visibility into costs or profit. This monitoring gap is where bad decisions happen: over-investing in unprofitable features, under-scaling profitable ones, missing cost optimization opportunities. Learn to build cost + profit monitoring. This transforms AI from a cost center into a measurable business investment you can defend and optimize.

You'll learn from

Mahesh Yadav

Ex AI Product Lead -Google l Meta l Microsoft l AWS | 10k+ Alums l Founder - Agentic AI Institute

Mahesh Yadav brings 20+ years of experience building AI products at Google, Meta, AWS, Microsoft. He holds 12 patents in AI training, power management and computer vision. He has launched major agentic-AI initiatives (for example launching an agent for AWS Bedrock, featured in CEO keynote) and trained thousands of professionals to succeed in AI roles. With this programme you get rare access to CEO-level of mentorship.
Substack AI PM Newsletter l Linkedin Community of AI PMs l YouTube For Free Sessions Recordings
Currently, he is building back-office AI agents for the enterprise, starting with in-house legal teams through LegalGraph.AI. His work bridges education and real-world AI deployment, helping organizations adopt agentic systems that automate complex knowledge work.

Previously at

Google
Meta
Microsoft
Amazon Web Services
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