The world of artificial intelligence is buzzing with the promise of AI agents—autonomous systems that can understand, reason, and act to automate complex tasks. Over the past two months, three compelling agent platforms have emerged for building and deploying AI agents: Databricks Agent Bricks, AWS AgentCore, and Gensee.
While all three products are in the space of AI agent platforms, they have different use cases, features, strengths, and weaknesses. We have tested each platform with real agent examples. In this blog post, we provide a comparison of them and delve into each platform's technical details. We chose these three platforms because of their overall diversity, similarity in certain features, and their close release dates; there are other agent platforms similar to each of them that we do not include here.
Overall Comparison
| Feature | Databricks Agent Bricks | AWS AgentCore | Gensee |
|---|---|---|---|
| Use Cases | Four types of data analytics | General | General |
| Input Interface | No-code | Annotated code | Unchanged code |
| Autoscaling | ✅ | ✅ | ✅ |
| Cost and Performance Optimization | ✅ | ❌ | ✅ |
| Quality Improvement | ❌ | ❌ | ✅ |
| Downloadable Optimized Code | ❌ | ❌ | ✅ |
| Model and Tool Selection | ❌ | ❌ | ✅ |
| Metrics Automation | ✅ | ❌ | ✅ |
| Test Case Automation | ❌ | ❌ | ✅ |
| Context Management | ❌ | ✅ | ❌ |
| Integration with Other Services | ✅ | ✅ | ❌ |
| Billing Model | By individual services | By individual services and compute/memory | By token |
| Optimization Cost | $100+ per optimization | N/A | Free, $5 per download |
| Model Calls Included? | ❌ | ❌ | ✅ |
| Free Credits | $40 total | ❌ | $10 per month |
| Targeted Users | Data scientists | Enterprise developers | Non-enterprise developers |
Databricks Agent Bricks: No-Code Data-Centric Agent Creation
Databricks Agent Bricks is a domain-specific agent framework catered towards Databricks users. The core philosophy behind Agent Bricks is to leverage the wealth of data already stored and managed within Databricks Lakehouse to build AI agents that can be used for understanding user data.
Key Features:
- Data-Centric Agents: Supports four types of agents: information extraction, custom LLM, knowledge assistant, and multi-agent supervisor. All data must pre-exist in the user's Unity Catalog.
- No-Code Agent Creation: Users define tasks in natural language. AgentBricks generates agents automatically, though the code is not visible or downloadable.
- Automated Metrics and In-Depth Analysis: Generates metrics based on user tasks and data, providing a detailed evaluation scoreboard.
- Automated Cost and Throughput Optimization: Automatically optimizes generated agents for cost and throughput, though this process can be lengthy and expensive.
- Unified Governance: Inherits robust governance and security from the Databricks platform, including Unity Catalog.
Strengths & Limitations:
Strengths
- Ease of use with a no-code interface.
- Accelerated development lifecycle.
- Seamless integration with Databricks Lakehouse.
- Unified platform for data, analytics, and AI.
Limitations
- Primarily for organizations in the Databricks ecosystem.
- Limited to four agent types.
- Lack of transparency and deep customization.
- Can be costly and opaque in its billing.
AWS AgentCore: Enterprise-Grade Agent Deployment
As part of the Amazon Bedrock family, AgentCore provides the foundational infrastructure and tools necessary to deploy and scale enterprise-grade AI agents with high degrees of flexibility and security.
Key Features:
- Light Annotation on Generic Frameworks: Works with any agent framework (CrewAI, LangGraph, etc.) by requiring simple code annotations.
- Autoscaled Agent Serving: Deployed agents and tools are autoscaled in a serverless fashion.
- Context and Memory Management: Fully-managed context and memory services for both short-term and long-term persistence.
- Tool Deployment: Deploy custom tools or use pre-built ones like a browser runtime and code interpreter.
- Enterprise-Grade Security: Built with security and compliance for enterprise use, featuring session isolation and AWS IAM.
Strengths & Limitations:
Strengths
- High flexibility and control for custom development.
- Scalable and low-latency performance.
- Flexible context and memory management.
- Deep integration with the AWS ecosystem.
Limitations
- High complexity in setup and development.
- Requires manual optimization and testing.
- Complex, fine-grained billing model.
Gensee: Developer-Oriented Agent Optimization and Serving
Gensee enters the market with a single goal: helping developers quickly productionize their AI agent prototypes. As such, Gensee offers a platform for developers to deploy, test, evaluate, automatically improve, and launch their AI agents.
Key Features:
- Framework-Agnostic: Integrates with agents written in any framework (or none) via zip package, GitHub URL, or Docker image.
- Automated Optimization: Automatically optimizes agents for quality, cost, and latency, with downloadable optimized code.
- Automated Metrics and Testing: Generates both metrics and customized test cases for user agents.
- Full Control in Model/Tool Tuning: Allows manual testing and swapping of models/tools in addition to automated optimization.
- Autoscaled Serving: Autoscales deployed agents in secure execution environments.
Strengths & Limitations:
Strengths
- Automates the time-consuming optimization process.
- Developer-centric and transparent.
- One-click, flexible deployment.
- Automated testing and evaluation.
- Generous free tier ($10/month credit).
Limitations
- Early-stage product, may evolve rapidly.
- Limited third-party service and tool support.
- No hosted context or memory management yet.
The Future of AI Agents
The emergence of platforms like Databricks Agent Bricks, AWS AgentCore, and Gensee highlights the rapid maturation of the AI agent ecosystem. The choice between them will ultimately depend on your organization's specific needs, existing infrastructure, and technical expertise. Whether you need a fully integrated data-to-agent pipeline, a flexible set of infrastructure building blocks, or a powerful optimization engine, there is now a platform designed to help you on your journey into the exciting world of AI agents.