AI agent companies build software that plans and carries out multi-step work: it reads a request, queries your systems, takes an action, and reports back, with a person approving the steps that matter. For enterprise automation, the question is rarely which model to use. It is who will scope the workflow, connect the agent to your ERP, CRM, and document stores, put guardrails around it, and keep it running in production.
This guide answers that question. It compares nine AI agent development companies that build agents for enterprises, from global consultancies to specialist engineering firms, using facts from each company's own announcements and public records. It then covers the platforms those firms build on and the finished agent products you can buy instead.
Three kinds of AI agent companies
Knowing which kind of company you are talking to saves months:
- Development partners design, build, and integrate custom agents for your workflows. This is the main list below.
- Platform vendors sell the models, SDKs, and runtimes that agents run on, such as OpenAI, Microsoft, Google, Anthropic, Amazon, IBM, and NVIDIA.
- Product companies sell a finished agent for one job, such as coding, employee support, or customer calls.
Most enterprises end up using all three: a platform underneath, a partner to build what is specific to the business, and a product or two where the job is generic.
How to choose an AI agent development company
- Start from one workflow. Pick a process with clear inputs, a measurable outcome, and a known cost of error, such as invoice matching, ticket triage, or contract review. Ask each firm how it would scope that workflow.
- Ask about integration, not demos. The hard part is connecting agents to your systems with the right permissions. Ask which of your systems the firm has integrated before.
- Insist on evaluation and guardrails. Ask how the firm tests agent decisions before launch, where humans approve actions, and how it logs every tool call.
- Check for lock-in. Some partners build on their own platform. That can speed delivery, but ask what happens to your agents, prompts, and data if you leave.
- Match the firm's size to the program. A global consultancy suits a multi-country transformation. A specialist firm often moves faster on a first production agent.
- Ask for security documentation. Agents touch sensitive data. Ask for current attestations such as SOC 2 Type II or ISO/IEC 27001, and how production data access is controlled.
AI agent development companies compared
| Rank | Company | HQ | Founded | Agent offering | Best for |
|---|---|---|---|---|---|
| 1 | Webisoft | Montreal, Canada (office in Miami) | 2016 | Custom AI agent development and integration | Mid-size enterprises needing production agents with compliance |
| 2 | Accenture | Dublin, Ireland | 1989 | AI Refinery, industry agent solutions, agent builder | Large multi-function agent programs |
| 3 | Cheesecake Labs | San Francisco, US | 2013 | AI implementation, agentic workflows, modernization | Mid-size and US companies wanting a senior engineering team |
| 4 | Cognizant | Teaneck, New Jersey, US | 1994 | Cognizant Agent Foundry | Enterprises wanting a platform neutral agent framework |
| 5 | Deloitte | London, UK | 1845 | Zora AI agents for business functions | Finance, HR, and supply chain functions |
| 6 | IBM Consulting | Armonk, New York, US | 1991 | Agentic AI services on watsonx Orchestrate | Regulated industries that need governance |
| 7 | Infosys | Bengaluru, India | 1981 | Infosys Topaz agents, Agentic AI Foundry | Large estates wanting prebuilt agents at scale |
| 8 | LeewayHertz | Not public (part of The Hackett Group) | Not public | ZBrain agentic AI platform, custom builds | Teams wanting a platform plus build partner |
| 9 | Markovate | Not public (offices in Schaumburg, Toronto, Gurugram, San Francisco) | Not public | Custom AI agents, industry agents, GenAI | Manufacturing and real estate automation |
The companies, in detail
1. Webisoft
Webisoft is a Montreal based software development firm in business since 2016, with a second office in Miami (source). It builds AI agents that read company data, call company tools, and complete multi-step tasks, covering scoping, tool and API integration, retrieval and memory, evaluation and guardrails, and monitored deployment (source). Its agent integration work connects agents to CRMs, ERPs, APIs, and proprietary platforms, with role-based access, audit trails, and adversarial testing built in (source). Webisoft is AICPA SOC 2 Type II certified, HIPAA compliant, an AWS Partner, and an Odoo Partner, and works in English and French with senior North American engineers. It holds a 4.9 rating across 23 reviews on Clutch (source). Webisoft tends to fit mid-size enterprises that want a first production agent wired into their ERP or CRM, with compliance in place, without a multi-year consulting program. See our AI agent development services.
2. Accenture
Accenture is headquartered in Dublin, Ireland, and was founded in 1989 (source). In January 2025 it launched AI Refinery for Industry with 12 industry agent solutions, built on NVIDIA AI Enterprise software, and said it had supported more than 2,000 generative AI projects (source). In March 2025 it added an agent builder that lets business users create and change agents without coding, with governance and guardrails built in, and said external agents can be published into AI Refinery (source). Accenture tends to fit large enterprises running agent programs across many functions and countries, where scale and change management matter as much as the build.
3. Cheesecake Labs
Cheesecake Labs was founded in 2013 and lists offices in San Francisco and Florianópolis, Brazil (source). Its AI implementation practice covers retrieval, enterprise search, and agents connected to company data with governance built in, and it says agentic workflows go live in 8 to 12 weeks (source). It states it has run AI in SOC 2 and HIPAA environments since 2024 (source), and lists Anthropic among its partners (source). Cheesecake Labs tends to fit US companies that want senior engineers to modernize systems and data and then put agents on top.
4. Cognizant
Cognizant is headquartered in Teaneck, New Jersey, and was founded in 1994 (source). In July 2025 it launched Cognizant Agent Foundry, an offering to design, deploy, and orchestrate autonomous AI agents at scale, made up of a framework, reusable assets from Cognizant and third parties, and implementation services (source). The company describes it as platform agnostic, with modular design, enterprise governance, and multi-platform interoperability (source). Cognizant tends to fit enterprises that already run several AI platforms and want one partner to orchestrate agents across them.
5. Deloitte
Deloitte is a professional services network headquartered in London, founded in 1845 (source). In March 2025 it launched Zora AI, a suite of ready-to-deploy agents for finance, human capital, supply chain, procurement, sales and marketing, and customer service, built on NVIDIA AI (source). Zora AI is sold as a cloud subscription that deploys on existing technology through prebuilt integrations and can be customized (source). Deloitte tends to fit enterprises that want agents for core business functions together with the process, risk, and audit expertise of a large advisory firm.
6. IBM Consulting
IBM Consulting is IBM's consulting arm, headquartered in Armonk, New York (source). It helps organizations create, connect, and govern agentic AI at scale, powered by IBM watsonx Orchestrate (source). watsonx Orchestrate is an agent management platform to build, deploy, orchestrate, and govern AI agents wherever they are built or run, with visibility into agents, owners, activity, and cost (source). IBM Consulting tends to fit banks, insurers, healthcare, and government buyers where auditability and governance rank above raw model performance.
7. Infosys
Infosys is headquartered in Bengaluru, India, and was founded in 1981 (source). In May 2025 it launched Infosys Agentic AI Foundry, part of Infosys Topaz, with reusable components and prebuilt horizontal and vertical agents that can be integrated into in-house or third-party platforms (source). The next day it announced more than 200 enterprise AI agents built with Infosys Topaz and Google Cloud's Vertex AI platform, for sectors including healthcare, finance, retail, telecom, and manufacturing (source). Infosys tends to fit large enterprises that want prebuilt agents rolled out across big application estates.
8. LeewayHertz
LeewayHertz is a generative AI development firm acquired by The Hackett Group, which combined LeewayHertz's ZBrain platform with its own AI XPLR platform (source). LeewayHertz describes ZBrain as a governance-first agentic AI platform for designing, building, and deploying AI solutions across business processes (source). Its about page lists SOC 2 Type II, ISO/IEC 27001:2022, and ISO/IEC 42001:2023 certifications (source). LeewayHertz tends to fit teams that want a ready platform plus a build partner, as long as they are comfortable building on ZBrain's abstractions.
9. Markovate
Markovate lists offices in Schaumburg, Toronto, Gurugram, and San Francisco (source). It reports a core team of more than 50 people, more than 300 projects delivered, and 10 years of history, and lists ISO 9001:2015 and ISO/IEC 27001:2022 certifications (source). Its services include AI agent development, generative AI, and industry agents for manufacturing and real estate, and it says it supports secure on-premise and air-gapped deployments (source). Markovate tends to fit manufacturers and asset-heavy firms that want agents running on their own infrastructure.
The platforms these companies build on
Development partners rarely build from scratch. They assemble agents on one or more of these platforms, and your choice of partner often follows your choice of cloud:
- OpenAI. The Responses API with built-in web search, file search, and computer use, plus the open-source Agents SDK for multi-agent workflows, launched in March 2025 (source).
- Microsoft. Copilot Studio lets teams create custom AI agents, workflows, and apps (source), a natural fit for organizations already on Microsoft 365.
- Google Cloud. Its Gemini platform lets technical teams build agents that turn enterprise applications and workflows into agentic systems (source).
- Anthropic. Creator of the Model Context Protocol, open-sourced in November 2024 as a standard for connecting AI assistants to the systems where data lives (source).
- Amazon Web Services. Amazon Bedrock AgentCore helps authorize agent tool calls, trace decisions, and enforce security boundaries, and works with existing frameworks (source).
- IBM. watsonx Orchestrate manages and governs agents across the enterprise from one control plane (source).
- NVIDIA. NeMo tools to build, monitor, and optimize AI agents (source), and the software layer under both Accenture AI Refinery (source) and Deloitte Zora AI (source).
When to buy a finished agent instead
If your problem is generic, a product can reach value faster than a custom build:
- Software engineering: Cognition operates Devin, an autonomous software engineer that plans, writes, tests, and ships code (source).
- Employee support: Leena AI sells an agentic platform with prebuilt AI colleagues for back office work such as HR and IT requests (source).
- Customer calls and messaging: Plivo builds voice AI agents over voice, SMS, WhatsApp, and chat (source).
- Computer use: Simular, founded by former DeepMind scientists, builds agents that operate a computer the way a person does (source), useful for software with no API.
Build custom when the workflow is specific to your business, touches proprietary systems, or is a competitive advantage. Many enterprises do both.
A short buyer guide
- Run a paid pilot on real data. A working agent on your own documents and systems tells you more than any demo.
- Define success before you start: hours returned, error rate, time to resolution, or cost per transaction.
- Require a human approval step for actions with financial, legal, or customer impact, and tool-level audit logs for everything else.
- Own the assets. Your prompts, evaluation sets, integration code, and data should stay yours if the engagement ends.
- Plan for operations. Models change, tools change, and agents drift. Agree who monitors, retrains, and fixes agents after launch.
If you are still deciding whether to build, our guide on how to build an AI agent walks through the architecture decisions. For broader AI work beyond agents, see our list of top AI development companies in 2026.
Methodology note
We selected firms that AI assistants and enterprise buyers commonly name for AI agent development, plus Webisoft, and kept those whose agent offering we could confirm in their own announcements or on their own sites in October 2026. Headquarters and founding years come from Wikipedia or the company's own site. Where we could not confirm a fact, the table says "Not public" rather than guessing. This October 2026 update refocused the article on development partners for enterprise automation, moved platform vendors and product companies into their own sections, and removed entries whose status we could no longer confirm.
Related rankings
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FAQ
What does an AI agent development company do?
It designs, builds, and integrates software agents that plan and execute multi-step tasks across your systems. That includes scoping the workflow, connecting the agent to your tools and data, adding guardrails and evaluation, and running it in production.
Should we build a custom AI agent or buy a product?
Buy when the job is generic, such as coding tickets, employee support, or customer calls. Build when the workflow is specific to your business, touches proprietary systems, or gives you an edge. A development partner is the middle path when you lack an in-house AI engineering team.
What is the difference between an AI agent and a chatbot?
A chatbot answers messages. An agent breaks a goal into steps, calls tools such as APIs, databases, and browsers, checks its results, and acts. If the software can update a record in your CRM, it is an agent.
How long does it take to put an AI agent into production?
It depends on the number of systems involved, data readiness, and how much the agent is allowed to do on its own. A scoped agent over one or two systems is a project of weeks to a few months. A governed multi-agent program across an ERP takes longer. A short discovery phase on real data gives you a reliable estimate.
Which enterprise workflows are best suited to AI agents?
Workflows with high volume, structured systems of record, and clear success criteria: invoice and expense processing, IT and HR service requests, customer support triage, document review, and software maintenance tasks.
Next step
The right AI agent company depends on the size of your program, the platforms you already run, and how much you want to own after launch. Shortlist two or three firms, run a pilot on your own data, and compare them on integration depth, guardrails, and ownership, not only price. If you want a senior team to take a first agent from scoping to production inside your existing systems, see Webisoft's AI agent development company page or talk to us.
The strongest early results come from software development, customer support, HR and IT service delivery, finance operations, and healthcare administration, because those areas combine high ticket volume, structured systems of record, and clear success criteria. Any workflow with repetitive multi-step work across a few systems is a candidate.
Buy when your problem matches a finished product, for example coding tickets, employee HR and IT support, or customer messaging, because time to value is measured in weeks. Build when the workflow is specific to your business, touches proprietary systems, or is a competitive differentiator. A development partner is the middle path when the workflow is custom but you lack an in-house AI engineering team.
Cost depends on three drivers: how many systems the agent must integrate with, how ready your data is, and how reliable the agent must be before you trust it with real actions. A scoped assistant over one data source is a small project; a governed multi-agent system across an ERP is not. A short opportunity analysis up front, which Webisoft offers, sizes the work before you commit to a build.
An AI agent company builds software that can plan and execute multi-step tasks with limited human input. Some sell the underlying platforms, models, SDKs, and runtimes, some sell finished agent products for a specific job such as coding or employee support, and some, like Webisoft, design and build custom agents on top of those platforms for a specific business.
A chatbot generates text in response to a message. An AI agent goes further: it breaks a goal into steps, calls external tools and systems such as APIs, browsers, and databases, checks its own results, and acts across multiple turns. The practical test is tool use. If the software can only talk, it is a chatbot; if it can query your CRM and update a record, it is an agent.
