Databricks Inc.

06/17/2025 | News release | Distributed by Public on 06/17/2025 12:12

Leveraging Agentic AI in Games

Introduction

Studios have years of experience building sophisticated, dynamic systems that work within the constraints of game development. Even with that in mind players want more. Players want more dynamism, control and replayability. They want game worlds that are more dynamic, characters that feel alive, and experiences that are truly interactive and personalized. Studios hear this loud and clear, and so do we. At the end of the day, our shared goal is simple: to make amazing games for players. We achieve this goal by establishing a shared understanding that respects the expertise already in the industry and focuses on solutions that actually help studios deliver the experiences players want.

Agentic AI systems can help game developers: create highly dynamic game worlds, NPCs that can react to the player, QAgents that speed development and produce higher quality outcomes for player support requests. Agentic systems can also be applied to line-of-business problems like generating personalized marketing creative. Too often, conversations regarding new technologies and capabilities focus on buzzwords and big promises, without fully appreciating the technical artistry and practical realities that go into making great games. The opportunities that we'll share in this blog will range from: something you can do today with relative ease to more advanced future opportunities.

Before delving into the content, we would be remiss if we didn't discuss our nomenclature. The words Artificial Intelligence (AI) can mean so many things in Games. The industry has built AI's in the form of NPCs and bots for quite a while. Procedural generation has also been leveraged to help create content since Games were a thing. When Machine Learning (ML) and Reinforcement Learning (RL) became more prevalent in the industry they were often referred to as AI as well. Now Generative AI (Transformer Based Models) is being discussed and referred to as AI. To clarify and simplify, this blog when we say AI we are referring to GenAI. If we are referring to any of the other terms, we'll name them specifically.

What is Agentic AI?

Agentic AI refers to autonomous, goal-driven artificial intelligence systems that can act independently, adapt in real time, and make complex decisions based on context and objectives. Unlike traditional, rule-based AI, which follows scripted behaviors or static routines, agentic AI is designed to learn, reason, and evolve within dynamic environments.

To build performant and scalable Agentic AI workflows, Games studios need to put their agents where their data is. Databricks offers the only unified platform for developing, evaluating, and governing AI Agents that deliver reliable, data-driven results in Games environments. By leveraging existing Databricks solutions, like AI Playground and MLflow Model Signatures to define agents' input and output schema, you can prototype agents right where your data lives.

Here is a quick look at what works and what does not:

What Studios Need Common Communication Mistakes What Works Better
Tools that integrate with existing engineering workflows Proposing total game code overhauls, or worse, an interconnected network of piecemeal tools that lack a cohesive data strategy Agent systems that are built into existing workflows and sit next to the game telemetry
Low-latency AI inference Relying on the game servers, or worse, game clients, for inference Lightweight models that run in real-time on compute adjacent to the game servers. For example, in Kubernetes sidecars.
Help with pre-release QA Promising reinforcement learning (RL) solutions with no thoughts for how to gather high-quality play data ahead of releases or a plan for how to scale it out to not slow down the build process Robust game experience and telemetry collection pipelines on scalable infrastructure and defect recognition systems to enhance human playtesting, scaled where possible with behavior cloning or RL-based automation.
Marketing creative that speaks to different player segments enticing high quality user acquisition Proposed systems are focused on generating large quantities of creative with the assumption that the goal is building final creative for marketers to "select from" failing to respect the creative team's value Systems that can extract details about the desired players for a campaign and then generate starter images, based on the studio's past creative, for marketers to create personalized creative that speaks to high-value segments
Databricks Inc. published this content on June 17, 2025, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on June 17, 2025 at 18:12 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at support@pubt.io