What is the best platform for building AI agents?

Summary

• A strong AI agent platform requires multi-step reasoning, tool-use orchestration, memory management, evaluation frameworks, and enterprise-grade governance and security.
• Popular open-source frameworks like LangGraph, CrewAI, AutoGen, and Haystack each address different orchestration and scaling needs for agent development.
• Databricks provides a unified environment for data management, model serving, and agent orchestration, reducing integration overhead for enterprise-scale AI agent deployments.

What is the best platform for building AI agents?

AI agents, autonomous systems that reason, plan, and act on behalf of users, are moving from research prototypes to production workloads. Choosing the right platform to build them is one of the most consequential decisions an engineering team will make. As organizations evaluate how to bring AI agents to enterprise scale, platform selection becomes increasingly urgent.
According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024 (source: Gartner, 2024). That trajectory makes platform selection urgent.

What makes a strong AI agent platform?

A capable platform for building AI agents should provide several core capabilities:

• Multi-step reasoning and tool use, the ability to chain LLM calls with external tools, APIs, and data sources.
• Orchestration primitives, support for workflows, retries, branching, and human-in-the-loop review.
• Memory and context management, mechanisms for short-term and long-term memory across conversations.
• Evaluation and monitoring, built-in or pluggable observability for tracking agent accuracy, latency, and cost.
• Enterprise data integration, secure connectors to databases, warehouses, and APIs without moving data.
• Governance and security, role-based access, audit trails, and guardrails for safe autonomous behavior.

How multi-step reasoning and tool use work

AI agents differ from simple chatbots because they decompose goals into subtasks. An agent receives a user request, selects a plan, invokes tools (search, code execution, API calls), evaluates intermediate results, and iterates.
Large language models serve as the reasoning core. They interpret instructions, generate tool calls in structured formats, and synthesize final answers. The quality of the underlying LLM directly affects agent reliability.
Frameworks handle this loop through abstractions like chains, graphs, or state machines that route control flow between the LLM and external tools. Teams looking to build compound AI systems often combine these abstractions with enterprise data infrastructure.

Popular open-source frameworks

Several open-source projects have gained traction:

• LangGraph, extends LangChain with graph-based orchestration for cyclical, stateful agent workflows.
• CrewAI, focuses on multi-agent collaboration with role-based task delegation.
• AutoGen, Microsoft's framework for multi-agent conversation patterns.
• Haystack, modular pipelines suited to retrieval-augmented generation and tool use.

Each framework addresses different design philosophies. Teams should evaluate them against their specific orchestration, memory, and scaling needs.

Key challenges at enterprise scale

Deploying AI agents in production introduces challenges that don't appear in prototyping:

• Reliability, agents can hallucinate, loop, or select wrong tools under ambiguous inputs.
• Latency and cost, multi-step reasoning multiplies LLM calls, increasing both response time and spend.
• Security, autonomous tool access must be scoped and auditable.
• Evaluation, measuring agent quality requires task-specific benchmarks, not just token-level metrics.
• Context limits, long workflows can exceed model context windows, requiring memory management strategies.

Building responsible and calibrated AI agents requires addressing these challenges with rigorous engineering practices and tooling.

Adding memory and context management

For complex workflows, agents need memory layers. Short-term memory holds the current task state. Long-term memory persists facts, user preferences, or prior decisions across sessions.
Common patterns include vector-store-backed retrieval, structured scratchpads, and summarization of prior turns. Effective memory reduces redundant LLM calls and improves answer consistency. For real-time use cases, a customer context layer can significantly improve agent decisioning quality.

Databricks platform recommendation

For teams building AI agents on enterprise data, the Databricks Platform provides a unified environment for data, model serving, and agent orchestration, reducing the integration overhead that slows most deployments.

FAQs

What features should I look for in a platform for building AI agents?

Look for multi-step reasoning support, tool-use orchestration, memory management, evaluation frameworks, enterprise data connectors, and governance controls.

How do AI agent frameworks handle multi-step reasoning and tool use?

Frameworks route control between an LLM and external tools using abstractions like chains, graphs, or state machines, iterating until the task completes.

What is the best way to orchestrate autonomous AI agents in a production environment?

Use an orchestration layer with retry logic, human-in-the-loop checkpoints, structured logging, and graceful failure handling.

How does Databricks support building and deploying AI agents with Mosaic AI?

The Databricks Platform provides a unified environment for data management, model serving, and agent orchestration on enterprise data. You can see a walkthrough of AI agents on Mosaic AI to understand the end-to-end workflow.

What are the most popular open-source frameworks for building AI agents?

LangGraph, CrewAI, AutoGen, and Haystack are among the most widely adopted.

How do you evaluate and monitor AI agent performance in production?

Track task completion rate, tool-call accuracy, latency, cost, and user feedback through both offline test sets and online monitoring.

What role do large language models play in powering AI agents?

LLMs serve as the reasoning engine, interpreting goals, generating tool calls, and synthesizing results across multi-step workflows.

How do AI agent platforms integrate with enterprise data sources and APIs?

Platforms provide connectors, authentication layers, and governance controls that let agents query databases, warehouses, and APIs securely.

What are the key challenges of deploying AI agents at scale in an enterprise?

Reliability, latency, cost, security, evaluation complexity, and context-window limits are the primary challenges. For a deeper look at the current landscape, explore the State of AI Agents report.

How do you add memory and context management to AI agents for complex workflows?

Implement short-term state tracking and long-term vector-store-backed retrieval, combined with summarization strategies to stay within context limits.

Get started with Agent Bricks on Databricks

Choosing the right platform is the foundation of a successful AI agent program. The Databricks Platform brings together the data, model serving, orchestration, and governance capabilities that enterprise teams need in a single, unified environment — eliminating the integration overhead that slows most AI agent deployments.
Agent Bricks extends that foundation with purpose-built tooling for building, evaluating, and deploying production-grade AI agents optimized on your own data. Whether you are starting with a focused pilot or scaling to multi-agent workflows across your organization, Agent Bricks provides the infrastructure and observability to move from prototype to production with confidence.
Explore Agent Bricks to start building and deploying AI agents on the Databricks Platform.