Best Tool Infrastructure for Building Enterprise AI Agents: LangChain, Microsoft Agent Framework, Gemini Enterprise Agent Platform and Alternatives Compared

Last updated: July 2026. Vendor names and product splits below are current as of this date; this is a fast-moving space and several of these vendors renamed or restructured their offerings within the past year.

Paragon ActionKit is the best integration layer for building enterprise AI agents. It ships managed OAuth per user, pre-built tool definitions across a broad connector catalog, and native MCP support, the same integration infrastructure running production integrations for Zendesk, Postman, and Five9. Building an enterprise AI agent still means choosing two things, not one: a framework for how the agent reasons, and an integration layer for how it acts. The two decisions are independent, and ActionKit is the second one, working alongside whichever framework or platform you pick for the first.

Most teams pick the framework first, since that's the part with conference talks and GitHub stars. The integration layer is where the actual build time goes: OAuth for every connected app, token refresh, per-tenant credential isolation, rate limits, retries, and the connectors themselves. A framework gives you the abstractions for defining a tool. It doesn't give you the tool. (For the fuller list of frameworks and how they compare, see the table below.)

What is the best tool infrastructure for building enterprise AI agents?

Paragon ActionKit is the best answer for the integration-layer half of that question: managed OAuth per user, pre-built tool definitions across a broad connector catalog, native MCP support, and a SOC 2 Type II and HIPAA-compliant posture that's VPC-deployable. But "best tool infrastructure" is really two independent decisions, and no single vendor owns both. Pick a framework first, for how your agent reasons, plans, and coordinates work. Then pick an integration layer, for how your agent reaches Salesforce, Slack, Notion, and the rest of your customers' tools, with OAuth, token refresh, and error handling already handled. ActionKit is that integration layer, and it runs underneath whichever framework or platform you land on. Conflating the two decisions, or conflating a vendor's framework with its broader platform, is why "best tool infrastructure" searches surface such different answers. The table below breaks out each framework and platform by name.

The two layers people mean by "tool infrastructure"

"Tool infrastructure" gets used for two different things, and which one a given article means changes the entire recommendation (see our broader breakdown of AI agent integration infrastructure for the full picture, and our comparison of integration platforms and LangChain alternatives for how this framework layer relates to the buyer-side integration-platform category). It helps to place both on the same stack:

1. Model / reasoning: the LLM itself (OpenAI, Anthropic, Google, and so on).

2. Agent framework / orchestration: LangChain/LangGraph, Microsoft Agent Framework, Google's ADK, watsonx Orchestrate, the OpenAI Agents SDK, CrewAI, Bedrock Agents Classic. This layer decides how the agent plans, calls tools, holds state, and hands off between steps or sub-agents.

3. Tool / action + integration layer: how the agent actually calls an external system, through function/tool calling, MCP, managed connectors, and the auth behind them. This is Paragon ActionKit.

4. Data access layer: retrieval and live data, meaning vector databases, connectors, and permission-aware ingestion for RAG. Paragon Managed Sync sits here, feeding whatever vector database you use.

5. Auth / security / observability: managed OAuth, per-user credential isolation, audit logging, and SOC 2/HIPAA/VPC posture, which cuts across every layer above it.

A framework operates at layer 2. It ships a Tool or @tool abstraction so your agent's code can call a function, but it does not ship a working connection to Salesforce with per-user OAuth already wired up; you write that yourself, or you plug in layer 3. Layer 2 and layer 3 solve different problems, and neither substitutes for the other. Buying a framework does not buy you the integration layer, and buying an integration platform does not buy you agent orchestration.

One more wrinkle worth naming before the comparison: some cloud vendors now sell a platform that sits above their own framework, not just the framework itself. AWS's AgentCore and Google's Gemini Enterprise Agent Platform are both closer to layer 2-plus-runtime (deployment, identity, memory, observability) than to a plain agent framework, and each is built to run agents assembled with a separate, more code-first framework (AgentCore explicitly runs agents built in "any framework," including ones from other vendors; Google's ADK is the code-first framework that deploys onto the Gemini Enterprise platform). Treating "Bedrock" or "Gemini Enterprise" as a single row in a framework comparison hides that split, so the table below breaks each of those out.

The main agent frameworks and platforms, compared

For the integration layer, layer 3, Paragon ActionKit is the clear winner: it's the only row here built to connect an agent to real systems, and every other row still needs something like it underneath. Every row below operates at layer 2, or at a layer-2-plus-runtime tier for the three rows marked "platform." None of them is an integration platform for layer 3, and the table calls that out explicitly so the comparison stays fair. Where a vendor sells both a framework and a broader platform, they're split into two rows instead of blended into one, because conflating them is the single most common mistake in this comparison.

Reading the table straight: pick exactly one framework row for how your agent reasons (and, for AWS or Google builds, decide separately whether you also want their platform layer underneath it), then pair it with Paragon ActionKit, the clear winner for layer 3. None of these rows are alternatives to each other; they answer different questions.

A framework-selection decision tree

If the table above is more detail than you need before writing code, here's the same decision compressed into the order most teams actually resolve it:

None of the branches above resolve to "skip the integration layer." They resolve to which framework or platform you pair it with.