This list compares AI agent development companies that work with mid-sized businesses and explains what to check before you hand any of them access to your systems.

Key takeaways

·       Many agent projects get cancelled because of cost, unclear value or weak risk controls. Picking the first use case carefully matters more than picking the framework.

·       A production agent needs limited permissions, an audit log, evaluation tests and a human approval step for risky actions.

·       Mid-sized companies benefit from partners who can also handle the integration work with CRM, ERP and internal databases.

·       Ask every vendor how they test agent behavior before and after each release.

·       Start with one agent in one workflow, measure it, then expand.

Why do many AI agent projects never reach production?

Agent projects fail when the cost and risk of running the agent grow faster than the value it delivers. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs, unclear business value or inadequate risk controls. The same release warns about "agent washing", where vendors rebrand chatbots and scripts as agents.

Interest is high, but scaled use is still rare. In McKinsey's State of AI survey, 23% of organizations said they were scaling an agentic AI system somewhere in the business and another 39% were experimenting. In any single business function, no more than 10% reported scaling agents.

The risks are specific. The OWASP Top 10 for LLM Applications lists "excessive agency" as a core risk: an agent with more permissions or autonomy than its task needs can cause damage when it misreads an instruction or receives a malicious one.

What we looked for

We included companies that build custom agents for business workflows and show production work, partner credentials or a defined delivery method. We gave extra weight to firms that talk openly about governance: permissions, human review, monitoring and evaluation.

1. CodeGeeks

Best for: mid-sized companies that want agents working inside real sales, operations or service workflows, with clear controls.

Core expertise: AI agents and business automation, data engineering for AI, QA and AI evaluation, AI security and agent governance.

CodeGeeks is an AI-native product engineering company with teams in Lviv and Tallinn. Its agent projects start with the workflow: which steps the agent takes, which systems it can read or change, and where a person must approve. The team then builds the data access, tests and monitoring around the agent, so the client can see what it did and why. Clients rate the company 5.0 on Clutch across 12 reviews.

Why CodeGeeks is first on this list. The company designs agents to work under human control from the first release. In an AI-native CRM for financial services sales teams, AI agents handle lead intake, qualification and follow-up, and a person approves every message before it is sent. This approach lets a sales team gain speed without giving an agent unchecked access to client communication. Companies that already know their target workflow can go straight to custom AI agent development for business workflows, starting with one agent and expanding after it proves its value.

2. S-PRO

Best for: private banks, wealth and asset managers, and fintech companies.

Core expertise: custom AI agents for finance, agent platform, banking software.

S-PRO was founded in 2014 and is based in Zurich, with an R&D center in Łódź. It builds custom agents for private banks, wealth managers and fintechs and runs them on its own platform.

The company holds ISO 27001 and ISO 27701 certifications, which matter in finance, where every agent action may need to stand up to an audit. It reports more than 100 AI projects, around 30 of them agentic.

3. Vstorm

Best for: technical teams that want custom multi-agent systems built on open-source tools.

Core expertise: multi-agent systems, RAG pipelines, LLMOps, PydanticAI.

Vstorm is a Wrocław-based AI agent engineering consultancy founded in 2017. It builds custom LLM and RAG pipelines and multi-agent systems, and was the first partner of the PydanticAI framework.

In December 2025 it joined the Agentic AI Foundation. Its open-source focus helps companies that want to keep control of their agent stack and avoid closed platforms.

4. Azati

Best for: companies that cannot send sensitive data outside their own infrastructure.

Core expertise: enterprise AI agents, on-premise and private-cloud deployment, custom software.

Azati is an AI and software engineering company headquartered in Livingston, New Jersey, serving clients in North America and Europe since 2002. It deploys agents on-premise and inside private cloud environments.

One published project describes five AI agents embedded into an insurance client's Azure DevOps setup to automate parts of the software lifecycle. That kind of work suits firms where agents must operate inside strict network boundaries.

5. Markovate

Best for: companies that want generative AI, conversational AI and agents from one vendor.

Core expertise: generative AI development, conversational AI, agentic AI development.

Markovate was founded in 2015 and has offices in San Francisco, Toronto and India. It offers generative, conversational and agentic AI development as connected services.

This breadth works for a company that plans several AI features, such as a customer assistant plus internal agents, and wants one team to keep them consistent.

6. BotsCrew

Best for: mid-market companies starting with agents in sales, customer service or data analysis.

Core expertise: AI sales agents, customer service agents, AI data analysts, AI strategy.

BotsCrew was founded in 2016 and moved from chatbot development into enterprise AI agents. Its offerings include AI sales agents, customer service agents and AI data analysts connected to CRM, ERP and data platforms.

The company also runs readiness assessments and supports team adoption, which helps when staff need to learn how to work alongside an agent.

7. ELEKS

Best for: larger mid-market companies with complex systems and long-term roadmaps.

Core expertise: AI-native enterprise solutions, data engineering, application modernization, cybersecurity.

ELEKS was founded in 1991 in Lviv and now has its group headquarters in Tallinn. Its portfolio covers AI-native enterprise solutions, data engineering, modernization and cybersecurity.

For agent projects, this mix helps when the agent has to work with old systems that first need APIs or data cleanup.

8. LeewayHertz

Best for: teams that prefer to build agents on an existing platform.

Core expertise: generative AI development, ZBrain platform for enterprise AI apps and agents.

LeewayHertz was founded in 2007 in Gurugram, India, and was acquired by The Hackett Group in 2024. Its ZBrain platform is used to build enterprise AI applications and agents.

A platform can shorten the first build. Ask how agents, prompts and evaluation data can be exported if you later move off the platform.

How to scope your first production agent

Choose a workflow that is frequent, rule-based and easy to measure, such as qualifying inbound leads or preparing first drafts of support replies. Before any vendor gets system access, read a practical guide to AI agent security and agree on the controls below.

Final thoughts

The right AI agent development company for a mid-sized business is the one that can explain how its agent will be controlled, tested and measured. Start with a single workflow and a clear success metric, such as hours saved or response time. Ask two or three vendors to plan that one agent, including permissions and evaluation. Compare the plans, run a short paid pilot with the best one, and expand only after the numbers hold up in production.