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2025 Gartner® thought leadership report

How to Choose the Right Architecture to Build AI Agents

Interest in AI agents has skyrocketed over the past 12 months. The Gartner® Software Engineering Survey for 2025 shows that “building AI capabilities into applications” ranks among both the top priorities and top challenges for AI engineering leaders.¹ But without a solid understanding of AI agent architecture, teams risk poor design choices that can lead to failed delivery and mounting technical debt.

In our view, this 2025 Gartner® report will guide your team in selecting the right components and architecture patterns for building AI agents, helping you to better understand:

  • The key components of AI agent architecture that reduce cost and risk
  • The benefits of agent roles, multi-agent modularity, and other AI agent architecture patterns
  • How to establish a well-defined AI agent architecture that ensures scalability, modularity, and adaptability to deliver agents that add business value

Download your complimentary copy, provided by Weights & Biases, and help aid your team in making the right architecture decisions for AI agents.

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1 Gartner®, How to Choose the Right Architecture to Build AI Agents, Tigran Egiazarov, Gary Olliffe, Adrian Leow, Jim Scheibmeir, Tom Coshow, Arun Batchu, 5 June 2025.

GARTNER® is a registered trademark and service mark of Gartner®, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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Research Engineer – Facebook AI Research
“For us, Weights and Biases was a game-changer. No other MLOps tool available allows for rapid iteration of AI experiments with the same ease of sharing results, annotating interesting behavior, and long-term storage of logging data.”
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VP of Product- OpenAI
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READ CASE STUDY

A Case Study with TRI

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Overview

Toyota Research Institute’s mission is to build the safest mobility in the world. Machine learning teams at TRI are pursuing autonomous driving, and they use the Weights & Biases system of record to make their models reproducible.
  • Company size: 300+
  • Industry: Autonomous vehicles
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Problem

Led by Adrien Gaidon, the ML team built up world-class infrastructure for training models, but lacked a good way to track and version the valuable results.
They quickly realized the need for a central system of record, but building a solution internally was a distraction from the team’s core goals.
“It’s really hard for machine learning right now to provide any guarantees, statistical or otherwise, on how reliable it’s going to be. Putting in a safety critical system, it really has to work. How can we make it safe enough so that we can put it in cars and save lives instead of endanger lives.”
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Adrien Gaidon
Toyota Research Institute

Solution

The TRI team compared different solutions for their experiment tracking problem, and settled on Weights & Biases as the best platform to coordinate machine learning projects.

Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with Weights & Biases’ lightweight experiment tracking and visualization solutions.

The Weights & Biases dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.

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“You have to define the metrics clearly when you have a robotic system or a self-driving car that is extremely hard to test on the public roads for instance because the safety standards are very high, but at the same time you want continuous deployment and you want rapid iteration.”
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Adrien Gaidon
Toyota Research Institute

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