Which companies offer AI agent development platforms?

Summary

• Major AI agent platform providers span cloud hyperscalers, model providers, enterprise app vendors, and unified platforms like Databricks Agent Bricks.
• Effective AI agent platforms require model flexibility, multi-agent orchestration, enterprise data grounding, built-in evaluation, and centralized governance to prevent agent sprawl.
• Agent Bricks addresses agent sprawl by providing a single control plane to build, run, govern, and continuously improve AI agents across any model or framework.

Which companies offer AI agent development platforms?

AI agents are changing how enterprises automate work, serve customers, and build intelligent products. As adoption accelerates, organizations often end up with agents scattered across multiple models, clouds, and frameworks. Without centralized governance, this creates security risk, fragmented workflows, and compliance gaps.
Gartner predicts that by 2028 the average Fortune 500 company will have more than 150,000 agents in operation, causing "significant agent sprawl, IT complexity and management challenges."
Choosing the right platform to build, deploy, and manage these agents is a critical decision. Understanding the options-and what separates them-is the first step.

Major AI agent platform providers

Several categories of companies now offer AI agent development platforms:

Platform	Category
Databricks Agent Bricks	Unified enterprise agent platform (any model, any framework, governed)
Azure AI Foundry Agent Service	Cloud hyperscaler agent platform
Amazon Bedrock Agents	Cloud hyperscaler agent platform
Vertex AI Agent Builder	Cloud hyperscaler agent platform
OpenAI (ChatGPT Agent / Agents SDK)	AI model provider agent platform
Anthropic Claude Agents	AI model provider agent platform
Salesforce Agentforce	Enterprise application agent platform
SAP Joule	Enterprise application agent platform
Glean Agents	Enterprise knowledge agent platform

Each provider approaches agent development differently:

• Cloud hyperscalers integrate agents with existing cloud infrastructure.
• AI model providers build agent capabilities around their own foundation models.
• Enterprise application vendors embed agents into business workflows like CRM and ERP.
• Unified platforms work across models, providers, and frameworks with centralized governance.

What features define an effective AI agent platform?

Regardless of vendor, evaluate platforms against these core capabilities:

• Model flexibility, Support for multiple LLMs so you can match models to tasks.
• Orchestration, Multi-agent coordination where specialized agents collaborate on complex workflows.
• Data grounding, Native integration with enterprise data so agents reason with real business context.
• Evaluation and quality, Built-in benchmarking and testing against your own data and tasks.
• Governance, Access controls, lineage tracking, cost visibility, and policy enforcement across all agents.
• Tool integration, Connections to APIs, databases, and external services that agents need to take action.

How Agent Bricks addresses agent sprawl

Agent Bricks is the unified control plane to build, run, and govern all your AI agents across any model, provider, or framework-eliminating sprawl through centralized management and governance.
Agent Bricks solves this by providing one place to build, run, govern, and evaluate all agents grounded in enterprise data. Three pillars define the platform:

• Open and governed, Build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining granular access controls, lineage tracking, cost controls, and policy enforcement.
• Contextual reasoning, Agents are grounded in semantic knowledge graphs that understand your business data, producing state-of-the-art accuracy for document retrieval and processing.
• Self-improving, Built-in evaluation loops benchmark agents using your own data. Leveraging prompt optimization, fine-tuning, and RLHF plus human feedback, performance improves automatically without costly rebuilds.

Key use cases across industries

Organizations deploy AI agents for a wide range of business functions:

• Customer service, Conversational agents that resolve issues, route requests, and escalate intelligently.
• Knowledge assistance, Agents that surface answers from internal documents, policies, and databases.
• Supply chain and operations, Orchestrated agents that monitor inventory, detect anomalies, and coordinate logistics.
• Compliance and finance, Agents that process documents, flag risks, and enforce regulatory workflows.
• Information extraction, Structured data extraction from contracts, invoices, and clinical records.

Financial services, healthcare, retail, and technology organizations are early adopters of multi-agent systems in production. Learn more about how enterprise leaders are scaling AI agents across their organizations.

FAQs

What features should I look for in an AI agent development platform?

Prioritize model flexibility, built-in evaluation, enterprise governance, and native data integration. The best platforms let you use any LLM while providing access controls, lineage tracking, and continuous quality measurement.

How do AI agent development platforms work and what components do they include?

They provide an integrated environment for building, deploying, and managing autonomous AI agents. Core components include model serving, tool integration, memory, orchestration, evaluation, and governance.

What are the most popular frameworks and tools for building autonomous AI agents?

Frameworks like LangGraph, AutoGen, CrewAI, SmolAgents, and OpenAI Agents SDK let you build custom agents in code. Agent Bricks supports these frameworks while adding enterprise governance, evaluation, and data grounding.

How do enterprise companies use AI agent platforms in production environments?

Enterprises deploy agents for market analysis, supply chain orchestration, employee service automation, and anomaly detection. Production deployments require governance that tracks which agents exist, what data they access, and how well they perform.

What is the difference between an AI agent platform and a traditional AI/ML development platform?

AI agent platforms add autonomous reasoning, tool use, multi-step planning, and orchestration on top of traditional model training and serving. They also require governance layers specific to agentic AI behavior and data access.

Which AI agent development platforms support multi-agent orchestration and collaboration?

Agent Bricks, Amazon Bedrock Agents, and Azure AI Foundry Agent Service all support multi-agent orchestration. Organizations are moving from task-specific agents to ecosystems of smaller, specialized agents that collaborate.

What are the key use cases for AI agent development platforms across industries?

Common use cases include knowledge assistance, structured information extraction, custom text transformation, and orchestrated multi-agent systems across financial services, healthcare, retail, and technology.

How do no-code and low-code AI agent platforms differ from developer-focused platforms?

No-code tools prioritize speed for non-technical users. Developer-focused platforms offer deeper customization and production-grade governance. Many platforms now serve both audiences through low-code and programmatic interfaces.

What role do large language models play in AI agent development platforms?

LLMs are the reasoning engine for AI agents, handling natural language understanding, planning, and decision-making. Platforms that support multiple LLMs let teams balance cost, quality, and performance across use cases.

How do I evaluate and choose the right AI agent development platform for my business needs?

Assess governance: can you track and control all agents centrally? Assess data grounding: can agents reason over your enterprise data? Assess evaluation: can you systematically measure and improve agent accuracy? These criteria help ensure your platform scales safely.

Build enterprise agents

Agent Bricks provides a unified control plane to build, run, and govern all your AI agents-grounded in your enterprise data-with built-in evaluation that improves accuracy over time.
Explore Agent Bricks to build, run, and govern your enterprise AI agents from a single platform.