Top 10 AI Agent Development Company Options to Consider in 2026

AI agents are moving beyond simple chatbots. They can look up information, use APIs, work with business systems, and handle tasks with limited human input. That makes them useful for customer support, sales, IT, operations, and internal knowledge management.

But if you want to build one, finding the right AI agent development company can be tricky. Some companies focus on enterprise AI, while others specialise in custom software, workflow automation, or connecting AI agents to the systems a business already uses.

We looked at 10 companies taking different approaches to AI agent development. We considered their technical capabilities, integration experience, use cases, security, delivery models, and the projects and information they have made publicly available.

The list is alphabetical, not a ranking. The right choice will depend on what you want the agent to do, what systems it needs to work with, and how much development and support you need after launch.

How We Selected These AI Agent Development Companies

Not every company using the term “AI agent” is doing the same kind of work. Some focus on autonomous agents and multi-agent systems. Others are software development companies adding agent capabilities to existing applications and workflows.

For this list, we looked at what each company actually offers rather than relying on general AI positioning. We considered its AI agent capabilities, development experience, integrations, published use cases, security approach, and delivery model. We also looked at company websites, product information, and case studies where available.

The result is a mix of AI specialists, software development companies, digital engineering firms, and larger technology providers. They are listed alphabetically rather than ranked.

Top 10 AI Agent Development Companies

1. Ascendion

Location: United States

Company type: Digital engineering and technology services

AI agent focus: Agentic AI, AI engineering, enterprise automation

Best suited for: Businesses bringing AI into existing software and business processes

Ascendion works across software engineering, AI, and digital transformation. Its work is relevant to businesses that want to add AI capabilities to software they already use rather than build everything from scratch.

For this type of project, the development work often goes well beyond the AI model itself. The agent may need to work with company data, APIs, internal applications, permissions, and existing workflows.

What to consider: Ascendion has a broad technology services offering, so it is worth asking about the specific team and AI agent projects that would be relevant to your business. A company’s overall AI capability does not necessarily tell you how much hands-on agent development experience a particular team has.

2. Azumo

Location: United States

Company type: Software and AI development company

AI agent focus: Custom AI agents, multi-agent systems, workflow automation

Best suited for: Businesses that need custom agents connected to applications, APIs, databases, or business processes

Azumo has a dedicated AI agent development offering covering autonomous agents, multi-agent systems, integrations, and human-in-the-loop controls. It works with frameworks including LangGraph, CrewAI, and Microsoft AutoGen.

Its published examples include voice operations, healthcare automation, and an AI sales development agent. The common thread is that the agent is built around a specific business task rather than being used simply as a conversational interface.

That makes the company worth looking at if your project involves connecting an agent to existing software or business data.

What to consider: Azumo’s approach is centred on custom development. If you are looking for a ready-made agent that can be configured with little engineering work, check the delivery model carefully before comparing proposals.

3. EPAM Systems

Location: Global

Company type: Digital engineering and technology services

AI agent focus: Enterprise agentic AI, AI engineering, workflow automation

Best suited for: Large organisations with complex systems and integration requirements

EPAM has been working on agentic AI for enterprise environments where agents need to operate within existing software, data, permissions, and business rules.

Its recent work includes agentic AI projects with Albert Heijn and 1&1, along with AI-driven workflows built around ServiceNow. The company puts a strong emphasis on controls around permissions, data, policies, and human oversight.

Those details matter once an agent is allowed to do something rather than simply answer a question. An agent that can update a record or trigger a workflow needs a very different level of control from an internal chatbot.

What to consider: EPAM’s enterprise delivery model may be more than a smaller project needs. If your use case is relatively contained, compare the proposed team, timeline, and level of service rather than the company’s full enterprise offering.

4. LeewayHertz

Location: United States

Company type: Software and AI development company

AI agent focus: Custom AI agents, multi-agent systems, generative AI

Best suited for: Businesses building custom AI applications around existing software or data

LeewayHertz develops custom AI agents as well as broader AI and software applications. Its agent work includes single-agent and multi-agent systems for tasks such as research, analysis, code generation, and business workflows.

The company also works with technologies such as CrewAI and AutoGen Studio. This gives businesses looking for a custom-built agent another option, particularly when the project involves more than a simple LLM integration.

What to consider: LeewayHertz has a wide software and AI portfolio. When evaluating it, ask to see projects that are close to your own use case and find out which people would actually be assigned to the work. A long technology list is less useful than relevant project experience.

5. Markovate

Location: United States

Company type: AI and digital product development company

AI agent focus: Agentic AI, workflow automation, decision intelligence

Best suited for: Businesses automating internal processes that involve information gathering, analysis, or decisions

Markovate’s agentic AI work covers workflow automation and systems that can pull information from different sources as part of a larger process.

One example is GraphiQ, an AI assistant described by the company as working with information from customer support mailboxes and SharePoint to help create FAQs.

This is a useful example of where agents can fit into everyday business work. Instead of having an employee search through several systems, collect the information, and prepare an answer manually, the agent can handle several of those steps.

What to consider: Ask how the proposed solution will be built and maintained, including which parts are custom and which rely on existing platforms or components. That distinction can affect both the initial cost and what you need to maintain later.

6. Master of Code Global

Location: Global

Company type: AI and software development company

AI agent focus: Production AI agents, conversational AI, workflow automation

Best suited for: Businesses that need an agent to work inside existing business systems

Master of Code Global covers the full development process, from deciding whether an agent is suitable for a particular use case through architecture, development, integration, deployment, and ongoing monitoring.

Its approach includes agents that can interpret goals, plan tasks, use approved tools and data, work through multi-step processes, and hand work back to people when needed. Security controls and monitoring are also part of the company’s production approach.

That matters because getting an agent to work in a demo is relatively easy compared with making it reliable in a live business environment. Real systems have failed API calls, incomplete data, permissions, exceptions, and users who do things the developers did not expect.

What to consider: Have the workflow mapped out before discussing the final scope. The more clearly you can explain what the agent should do, what systems it can access, and when a person needs to step in, the easier it is to compare proposals.

7. ONEXT DIGITAL

Location: Vietnam

Company type: Software development and IT outsourcing company

AI agent focus: Custom AI development, automation, LLM applications, RAG, business-system integration

Best suited for: Businesses that need a dedicated development team to build and maintain a custom AI agent

ONEXT DIGITAL approaches AI agent projects from a software development perspective. Rather than offering a standalone AI agent platform, the company works on custom software, AI applications, automation, and integrations with existing business systems.

Depending on the project, an agent can work with internal knowledge, APIs, CRM systems, databases, email, or other business applications. The development team can also work on the software around the agent, including backend, frontend, QA, and ongoing maintenance where required.

This model can be useful for businesses that already have software in place and want to add AI capabilities to it, rather than introduce another standalone platform.

What to consider: ONEXT DIGITAL is primarily a software development and outsourcing partner, not a packaged AI agent platform. If your main requirement is a ready-to-use agent product, another type of provider may be more appropriate.

8. ScienceSoft

Location: United States and global delivery

Company type: IT consulting and software development company

AI agent focus: Enterprise AI, custom AI agents, multi-agent systems

Best suited for: Organisations with complex, regulated, or industry-specific requirements

ScienceSoft has worked in AI and software development for many years and now offers custom AI agents and multi-agent systems for enterprise use cases.

Its published examples include AI agents for lending and healthcare, where security, governance, and compliance can be important parts of the project. The company also works with technologies such as OpenAI Agents SDK, Amazon Bedrock Agents, Google ADK, LangChain, and LangGraph.

The interesting part is not simply the technology stack. Different industries put very different demands on an AI agent. An agent used in lending, for example, needs to deal with data access and oversight in ways that may not matter for a simple internal knowledge assistant.

What to consider: ScienceSoft may be relevant for projects with more complex technical or compliance requirements. For a smaller automation project, compare the proposed scope and team with the actual level of complexity involved.

9. SoftServe

Location: Global

Company type: Digital engineering company

AI agent focus: Enterprise agentic AI, supply chain, procurement, operational workflows

Best suited for: Organisations looking at AI agents for complex operational processes

SoftServe has focused some of its agentic AI work on operational use cases, particularly supply chain and procurement.

Its Agentic AI Orchestrator is designed around activities such as planning, sourcing, rerouting, negotiation, and resolving supply chain issues. The company has also introduced an Agentic Catalyst programme aimed at helping organisations move from identifying agent use cases to putting them into production.

That focus is different from building an employee chatbot. Operational agents often need to work across several systems, deal with changing information, and know what to do when a process does not go as expected.

What to consider: SoftServe’s agentic AI work is largely aimed at enterprise and operational environments. Smaller businesses should look at the proposed project team and delivery model rather than assuming they need the full enterprise setup.

10. SoluLab

Location: United States and global delivery

Company type: Software and AI development company

AI agent focus: Custom AI agents, multi-agent systems, workflow automation

Best suited for: Businesses that need custom AI development across different applications or industries

SoluLab develops custom AI agents and multi-agent systems, including workflow orchestration, integrations, model optimisation, and ongoing support.

Its published technology stack includes OpenAI, Google Cloud, AWS, Azure, LangChain, Hugging Face, Anthropic, and Cohere. It also works across industries such as finance, healthcare, manufacturing, education, real estate, travel, and insurance.

The broad range of technologies and industries can be useful if you are still working out how an agent should fit into your product or business process. But it also makes it important to look beyond the company-wide portfolio.

What to consider: Ask for a project that is technically close to yours and find out who would be working on it. The experience of the actual team matters more than how many AI frameworks appear on the company’s website.

AI Agent Development Companies by Type

These companies are not all offering the same thing. Looking at them by type can make the shortlist a little easier to narrow down.

This is a way to understand the differences between providers, not a ranking.

How Much Does AI Agent Development Cost?

There is no useful single price for an AI agent. The development effort changes quite a bit depending on what the agent actually needs to do.

A simple internal agent that answers questions from a small knowledge base might be relatively inexpensive. Once the agent needs to access a CRM, call APIs, update records, remember context, follow approval rules, and work across several systems, the project becomes considerably more involved.

The cost is usually affected by:

• Number and complexity of workflows
• LLM and model usage
• RAG or other knowledge retrieval requirements
• APIs and systems that need to be connected
• Memory and context requirements
• Human approval and escalation rules
• Authentication and permissions
• Security and compliance requirements
• Monitoring and evaluation
• Hosting and infrastructure
• Ongoing maintenance

This is why it is difficult to compare AI agent quotes based on the headline price alone. Two vendors can give you very different numbers while technically offering very different scopes.

Before comparing proposals, make sure you understand exactly what each quote includes.

How to Choose an AI Agent Development Company

Start with the workflow, not the AI model. Write down what you want the agent to do from the first user request through to the final action. For example:

Customer request → identify intent → retrieve customer data → check order system → decide what to do → update CRM → send response → escalate if necessary.

Once the workflow is clear, you have something concrete to discuss with vendors.

1. Ask for similar projects

Look for experience that is close to your own use case. A company that has built an internal knowledge assistant may not have much experience with an agent that can modify customer records or trigger transactions.

2. Check integration experience

Most business agents need to work with other software. Ask which CRMs, ERPs, databases, communication tools, APIs, and internal systems the team has worked with.

3. Ask how permissions are handled

There is a big difference between an agent that can read information and one that can send emails, change records, approve transactions, or trigger workflows. Find out exactly what the agent will be allowed to do and when a human needs to approve an action.

4. Ask how the agent will be tested

A good demo does not tell you how an agent will behave in production. Ask how the team plans to test incorrect tool calls, missing information, hallucinations, failed integrations, unexpected user input, and changes to the underlying model.

5. Look beyond the development quote

The initial build is only part of the cost. Depending on the architecture, you may also have ongoing model usage, hosting, monitoring, API, support, and maintenance costs.

6. Find out who will actually build it

This matters particularly when you are dealing with a large consultancy. Ask who will be assigned to the project, how experienced they are with agentic systems, and whether the same team will remain involved after launch.

Read more: How to Choose an AI Development Company in Vietnam

Questions to Ask Before Hiring an AI Agent Development Company

Before signing a contract, ask:

1. Have you built an AI agent similar to this use case?
2. Which systems and APIs will the agent need to access?
3. Which actions can the agent take without human approval?
4. How will you test the agent before it goes into production?
5. How will errors and unexpected behaviour be monitored?
6. Which AI models and frameworks do you recommend, and why?
7. How will we control ongoing LLM and infrastructure costs?
8. Who owns the source code, prompts, workflows, and other project assets?
9. What happens if we want to change the model or development team later?
10. What support and maintenance are included after launch?

The answers will tell you much more about a vendor than a generic statement that it “builds intelligent AI agents.”

How to Evaluate an AI Agent Demo

When a vendor shows you a demo, do not stop at whether the conversation sounds natural.

Give the agent a realistic task and then make the situation slightly harder. Remove some information. Give it conflicting data. Make an API unavailable. Ask it to perform an action that should require approval.

A useful agent should be able to:

• Recognise when it does not have enough information
• Use the appropriate tool
• Avoid unauthorised actions
• Handle failed API calls
• Escalate to a person when necessary
• Keep track of the task
• Produce an audit trail where required
• Give reasonably consistent results across repeated tests

This tells you much more than a polished demo where everything goes exactly as planned.

Common Mistakes When Building AI Agents

AI agent projects can run into problems even when the underlying model performs well. The issues often appear around the workflow, integrations, permissions, or monitoring.

Giving the agent too much freedom

More autonomy is not necessarily better. For actions involving money, customer data, business records, or external communication, it may make sense to keep certain steps behind human approval.

Starting with the model

Choosing a model first and trying to find a use for it afterwards can make the project more complicated than it needs to be.

Start with the business task. Then decide which model, tools, retrieval method, and architecture make sense.

Treating an AI agent like a chatbot

A chatbot mainly responds to a user. An agent may need to plan a task, retrieve information, call tools, make decisions, and perform actions.

The testing and engineering requirements are therefore different.

Ignoring failure cases

An agent will eventually encounter incomplete data, a broken API, an ambiguous request, or an unexpected response.

Those situations should be part of the design and testing process from the start.

Measuring the wrong thing

The number of automated conversations does not necessarily tell you whether the project is working.

Depending on the use case, better measures might include task completion rate, resolution rate, handling time, human escalation rate, error rate, or cost per completed task.

Why Consider a Vietnam-Based AI Agent Development Company?

Vietnam is one option for businesses looking for an offshore or dedicated software development team. For AI agent projects, this model can work well when the business needs ongoing engineering rather than a one-off prototype.

A team may cover AI integration, backend and frontend development, QA, DevOps, API integration, and maintenance as the project develops.

The important thing is not simply where the team is located. Look at its experience with LLM applications, RAG, AI workflows, APIs, security, and production software. Communication, working-hour overlap, documentation, and ownership of the codebase matter too.

For Australian businesses, it is also worth confirming how the team handles time-zone overlap, project management, communication, and support before development starts.

Final Thoughts

AI agents are becoming a practical option for businesses that want to automate work across customer service, sales, operations, and internal processes. But choosing an AI agent development company is not just about finding a team that can connect an LLM to your software. You also need to think about integrations, permissions, testing, security, and what happens when the agent gets something wrong.

The companies in this list take different approaches. Some are better suited to large enterprise projects, while others focus more on custom development and dedicated engineering teams. Before making a decision, look at the projects they have actually delivered and ask how they would handle your specific workflow.

If you are exploring a custom AI agent for your business, ONEXT DIGITAL can help you work through the technical requirements, integrations, team setup, and development scope before you commit to a project.

Talk to our team about your AI agent requirements.