Analytics Insight’s just-released 2026 roundup of the United States’ top AI agent development companies puts San Francisco-based LeewayHertz at the top, citing more than a decade of enterprise AI work and a portfolio that now spans retrieval-augmented generation, custom copilots, and multi-agent systems. The ranking lands as Windows shops accelerate rollouts of Microsoft 365 Copilot and Azure AI services, making outside specialists like LeewayHertz newly critical for connecting prebuilt AI assistants to proprietary data and unique workflows.

The 2026 roundup and why LeewayHertz stood out

Analytics Insight’s annual AI agent development report evaluates firms on technical depth, enterprise track record, and capacity to deliver secure, production-grade agentic systems. LeewayHertz received top marks for its “decade-plus” history—a rare horizon in an industry where many competitors have only pivoted to agents in the last two years—and for demonstrating deployments that combine retrieval-augmented generation (RAG), large language model (LLM) orchestration, and autonomous multi-agent collaboration.

In plain terms, that means LeewayHertz isn’t just building chatbots that query a knowledge base. The firm constructs copilots that dynamically retrieve enterprise-specific information from SharePoint sites, SQL databases, and legacy ERP systems, then hand off complex jobs to a swarm of specialized agents that can check inventory, update records, and notify a human manager—all inside a Microsoft Teams channel or custom Windows application. The 2026 report singles out LeewayHertz’s work on “agentic RAG pipelines,” where retrieval doesn’t merely stuff documents into a prompt but actively plans multi-step searches across siloed data sources, as a differentiator that enterprise IT architects should pay attention to.

What “RAG, copilots, and multi-agent systems” actually mean for Windows users

For everyday Windows users, the technologies sound abstract, but they directly shape the tools you’ll soon use daily.

Retrieval-augmented generation (RAG)

RAG gives an LLM like GPT-4o or Microsoft’s own Phi models a live connection to your company’s files, emails, and records. Instead of training the model on that data—a risky and expensive move—RAG fetches only the relevant pieces at runtime. In a Windows environment, that could mean a copilot that answers “what were Q3 sales in the Northeast?” by pulling from the secure SQL Server instance your team uses every day, without ever exposing the entire database to the cloud. LeewayHertz’s approach, as noted in the roundup, layers in agentic planning so the copilot knows which data source to query first and how to cross-reference results.

Copilots

Microsoft’s Copilot brand has made the term ubiquitous, but the Analytics Insight report focuses on custom copilots—domain-specific AI assistants that go far beyond summarizing emails. LeewayHertz builds copilots that function as a new UI layer for enterprise software: a procurement copilot that drafts a purchase order from a natural-language request, checks budget availability via an API, and routes it for approval in Dynamics 365, all while running on a secured Windows endpoint. The ranking signals that off-the-shelf Copilot for Microsoft 365 is only the starting layer; specialty firms are now filling gaps that typical IT teams can’t bridge alone.

Multi-agent systems

Where a single copilot handles one task at a time, a multi-agent system deploys several AI agents that negotiate and divide labor. One agent might monitor an inbox for customer complaints, a second classifies urgency, a third drafts a response, and a fourth logs the interaction in a CRM. LeewayHertz’s multi-agent architecture, highlighted in the 2026 report, can run these agents across a hybrid setup—some on an on-premises Windows Server, some in Azure—so latency-sensitive tasks stay local while heavy LLM inference happens in the cloud. This hybrid pattern is increasingly what regulated industries demand.

What this ranking means for Windows admins and IT leaders

For IT pros managing Windows 11 fleets and Azure tenants, the LeewayHertz recognition is more than a vendor name to file away. It’s a signal that the ecosystem of serious, enterprise-grade AI agent developers has matured, and that your roadmap for 2026 and beyond should account for third-party specialist integrations.

Microsoft’s own Copilot stack—Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry—gives you a platform. But bridging that platform to the messy reality of decades-old SQL databases, proprietary .NET apps, and air-gapped environments often requires help from a firm that lives and breathes agent orchestration. LeewayHertz’s “decade-plus” experience, emphasized in the report, means it has likely encountered the same kind of legacy Windows infrastructure that large organizations still run. While the roundup doesn’t itemize every credential, the spotlight suggests the company understands the authentication (Active Directory, Entra ID), compliance (Windows Defender, Purview), and performance constraints that Windows administrators deal with daily.

Concretely, the ranking should push you to ask:
- Does our internal Copilot Studio agent really need to be built in-house, or could a partner like LeewayHertz deliver a more robust RAG architecture faster?
- Are we prepared to govern a multi-agent system that spans on-prem Windows servers and Azure VMs?
- How will we monitor the cost and latency of agents calling our on-prem data sources?

How we got here: from rule-based bots to agentic AI on Windows

Windows has been a platform for automation since the days of VBScript and PowerShell, but the leap to true agentic AI didn’t happen overnight. The progression looked like this:

  • 2016–2020: Enterprises deployed simple Q&A bots using Microsoft Bot Framework, often tied to SharePoint or Dynamics. These were rigid and required heavy manual rule-writing.
  • 2021–2023: LLMs like GPT-3 and then GPT-4 burst onto the scene, and “copilot” became the buzzword. Microsoft launched Copilot for Microsoft 365 in late 2023, giving millions of Windows users their first taste of generative AI at the OS and app level. But early copilots were largely self-contained; they could reason over emails, documents, and Teams chats, but not easily over a company’s custom ERP system or a legacy manufacturing database tucked away on a Windows Server 2019 box.
  • 2024–2025: The RAG pattern matured, and Microsoft responded with Azure AI Search and Copilot Studio, allowing IT teams to ground AI in their own data. Yet building production-grade RAG—especially when data sits behind firewalls or in on-prem SQL instances—demanded skills that few Windows admins possessed. This is when specialist AI agent development firms like LeewayHertz began carving out a niche.
  • 2026 (now): The Analytics Insight roundup marks a maturation milestone. The market now distinguishes between chatbot developers and companies that can architect secure, scalable, multi-agent systems that obey existing Windows security boundaries and governance policies. LeewayHertz’s top ranking reflects that shift.

Throughout this evolution, Windows itself has become a more AI-native OS. Windows 11’s 24H2 update shipped with a dedicated Copilot key on new PCs, and the underlying Windows Copilot Runtime gives local AI models access to hardware acceleration via NPUs. This local inference capability is crucial for scenarios where latency or data sovereignty prohibit round-tripping every query to the public cloud—exactly the kind of hybrid deployment LeewayHertz’s multi-agent designs can exploit.

What Windows users and IT pros should do now

For everyday Windows users

If you’re already using Microsoft 365 Copilot in Word or Teams, you may soon encounter custom agents built by partners like LeewayHertz. These agents often appear as plugins or bots inside Teams, or as specialized copilot panes in line-of-business apps. When that happens, pay attention to the permissions they request. Just like a mobile app, an AI agent accessing company files or databases should require explicit consent and should never hoover up data indiscriminately. The good news: LeewayHertz’s enterprise track record suggests a security-first posture, but always verify that your IT department has vetted any third-party agent before you connect it to sensitive data.

For IT administrators and architects

The immediate action is an inventory of your AI agent readiness. Ask your team:
1. Which on-prem or IaaS-hosted data stores (SQL Server, file shares, legacy apps) would deliver immediate business value if a copilot could query them? Map those to the authentication protocols (Kerberos, OAuth, certificate-based) that a third-party agent would need to navigate.
2. Does your Microsoft 365 tenant already have a catalog of approved AI agents? If not, create a pilot process that evaluates agent security, data residency, and compliance before they connect to production data. Use existing controls like Defender for Cloud Apps and Purview data loss prevention policies.
3. When evaluating a specialist firm like LeewayHertz, ask about its specific experience with Windows Server, Active Directory, and Azure hybrid join scenarios. Request reference architectures for multi-agent deployments that span on-prem and cloud.
4. Budget for agent management tooling. As multi-agent systems grow, you’ll need visibility into which agents are running, what they’re accessing, and how much they cost in LLM token consumption. Microsoft’s Copilot analytics dashboards and Azure AI’s cost management features will help, but expect to augment them with third-party monitoring if you go outside the pure-Microsoft stack.

For developers building on Windows

If you’re writing .NET or Python apps that will host agent-like features, study the patterns LeewayHertz is using. The roundup’s emphasis on “agentic RAG” suggests a move from naive RAG (just chunk and embed) to intelligent retrieval that plans multi-turn, multi-source lookups. Experiment with tools like Semantic Kernel or LangChain on Windows to understand how an agent can reason about which data to pull and in what order. Microsoft’s Copilot Studio also now supports multi-agent orchestration previews—worth exploring if your organization already runs on the Microsoft stack.

What to watch next

The Analytics Insight ranking is a single snapshot, but it points to a broader trend: the year 2026 will be remembered as the moment AI agent development moved from experimentation to enterprise-grade infrastructure. LeewayHertz’s top placement will likely attract attention from Microsoft itself—whether through partnership, investment, or acquisition—because Redmond is heavily reliant on partners to extend Copilot into the long tail of custom enterprise workflows. Expect more such rankings to emerge, and expect Windows to become the default canvas for these agents, bridging local NPU-powered AI with cloud-scale orchestration.

For now, the LeewayHertz nod serves as a practical reminder that the tools to build truly useful, secure, and autonomous AI agents are here—and they’re not locked inside a single vendor’s walled garden.