Agentic AI is the shift from software that answers questions to software that does work. An agent takes a goal, breaks it into steps, calls the systems it needs, checks its output, and hands off to a person when it should. For a mid-market company, that means a procurement agent that reconciles invoices against contracts, or a patient intake agent that pulls from the EHR and the scheduler. Our explainer on what agentic AI is and how it differs from chatbots and automation covers the fuller definition.
The hard part is not the model. It is getting an agent into production inside a company with a 15-year-old ERP, three overlapping SaaS tools, and a security team with real questions. That is what an agentic AI development company is for. Below are firms that do this work, what makes each different, and how to pick between them.
What to look for in an agentic AI development company
Review-site ratings and hourly rates say little about whether a firm can ship. Look at these five things instead.
Production track record
Ask how many agents the firm has running in production today, for how long, and what happened in the first 90 days after launch. Demos are cheap. A firm that has handled a model deprecation or a drifting agent has the scar tissue you want.
Integration depth
Agents are only as useful as the systems they can reach. A vendor should talk fluently about your ERP, CRM, data warehouse, and the custom app nobody wants to touch. If their case studies are all greenfield SaaS builds, expect friction. Our guide to AI integration consulting firms goes deeper.
Governance and guardrails
Who can trigger the agent? What can it do without approval? Where is the audit log? How is it evaluated before and after each change? A firm that cannot answer these in the first meeting will not answer them in production.
Model-agnostic architecture
The model market changes every quarter. A well-built agent system lets you swap the model without rewriting the orchestration, tools, or evaluation harness. If a vendor builds only on one cloud’s framework, you inherit that cloud’s roadmap and pricing. Our piece on model-agnostic AI and avoiding vendor lock-in explains why.
Senior involvement
At many firms, the partner who scopes the work disappears once the contract is signed. In agentic systems, decisions in week two shape what is possible in month six. You want senior engineers in the build, not just the pitch.
The list
Xcelacore AI
Xcelacore AI is an Oak Brook, Illinois firm founded in 2014 that builds and integrates agentic AI for mid-market companies, focused on hospitality, healthcare, commercial real estate, and education. Its model is Plan, Build, Deliver: an AI strategy and roadmap first, then custom agents and AI applications, then integration into the systems a company already runs and the training that gets people to use them. The architecture is model-agnostic and modular, so a client can change models or orchestration layers without rebuilding. The firm spent a decade shipping production systems before the agent wave, including cloud, integration, and QA work it now packages as AI Success Services. Senior partners who scope an engagement stay on the delivery team. Clients have included EY, Northwestern Medicine, and Great Wolf Resorts. It fits a CIO or CFO who wants one accountable partner from roadmap through adoption.
LeewayHertz
LeewayHertz is an AI consulting and development company that works with startups and Fortune 500 companies and cites more than 15 years of industry experience. It builds single-agent and multi-agent systems with frameworks such as CrewAI and AutoGen, and it offers ZBrain, a proprietary platform it describes as governance-first and model-agnostic, supporting agents built with LangGraph, Semantic Kernel, and Google ADK. Its listed industries include banking and finance, manufacturing, retail, logistics, healthcare, and technology. LeewayHertz was named a representative vendor in Gartner’s 2024 Hype Cycle for Generative AI, and The Hackett Group has announced an acquisition of the firm. For a mid-market buyer, the practical question is whether you are adopting ZBrain as a platform or hiring the firm for a custom build, since the two carry different long-term dependencies. Ask which one the proposal assumes.
Markovate
Markovate is a San Francisco-based generative AI company founded in 2015 with a presence in Toronto. It builds AI agents, copilots, enterprise chatbots, computer vision systems, and custom LLM integrations, and lists manufacturing, construction, healthcare, insurance, real estate, and finance among its industries. Markovate positions itself as a product-minded studio, a fit for a company that wants to turn an agent idea into a shippable internal product rather than a one-off automation. Its materials emphasize design and user experience alongside the AI build, which matters: agents employees do not trust do not get used. Pricing is not published. The fit is strongest when you have a clear use case and want one team to scope, design, and build it, and weakest if your main need is deep integration into legacy back-office systems.
DataArt
DataArt is a software engineering firm with offices in New York, London, Zug, and Munich and more than 25 years of operating history. Its AI practice runs from strategy and custom model development through generative AI and agentic systems, including multi-agent systems, RAG pipelines, LLM fine-tuning, and AI-assisted incident response. DataArt serves finance, healthcare and life sciences, CPG and retail, travel, media, manufacturing, and education. The firm also runs Artisyn, an AI-enabled operating model that uses delivery agents inside its own development lifecycle. Its depth in financial services and healthcare makes it a strong candidate when audit trails, human review, and compliance are first-order requirements. For a mid-market company, confirm the team is sized for your scope and not pulled from a large-account bench, and confirm who owns the agent code when the engagement ends.
HatchWorks AI
HatchWorks AI is an Atlanta-based firm founded in 2016 by Brandon Powell that describes itself as a pure-play AI company. It offers AI strategy and roadmaps, AI-driven development, and AI change management, with agentic automation, data modernization, and AI-native products as core deliverables. Engagement models include Agentic AI Pods, and its methodology, Generative-Driven Development, embeds AI into each step of the build. HatchWorks runs a nearshore model with teams in Costa Rica, Colombia, and Peru working in US time zones, plus offices in Chicago and Dallas. Listed industries include private equity, communications, technology, healthcare, finance, retail, and real estate. The nearshore structure can lower cost without the time-zone friction of offshore delivery. The change management offering is a real differentiator, since most agent projects fail on adoption rather than engineering.
West Monroe
West Monroe is a Chicago-headquartered, employee-owned business and technology consulting firm with a long mid-market and private equity client base. Its Agentic Transformation offering covers four disciplines: strategy and operating model, human and agent workflow design, technology and orchestration, and enablement and change. It is powered by Intellio Agent, an accelerator that identifies processes suited to automation, designs AI-native workflows, and produces build-ready plans inside a client’s existing cloud, data, and applications. West Monroe describes the offering as platform-neutral. The firm’s strength is the operating-model side: what work should change, who owns it, and how to govern agents across a company, not only how to build one. That suits a CEO or COO who needs the business redesign and the technology together. Expect consulting-firm pricing and a heavier engagement structure than a boutique builder.
Deviniti
Deviniti is a Polish IT services provider founded in 2004, headquartered in Wrocław with a second office in Warsaw. Its generative AI practice emphasizes secure, self-hosted AI applications that keep data under the client’s control, and it reports building and deploying an AI agent for Credit Agricole Bank that handles routine inquiries and routes complex cases to people. Deviniti also contributes to Bielik, an open large language model. Per its Clutch profile, its clients are mostly financial services and about half are mid-market companies, with a $25,000 minimum project size and $50 to $99 hourly rates. The firm has deep Atlassian experience from implementing Jira and Confluence, which shows up in its service-management agent work. For a mid-market company with strict data residency or self-hosting requirements, Deviniti is worth a call.
Vstorm
Vstorm is a Wrocław, Poland consultancy founded in 2017 that describes itself as an applied agentic AI engineering firm for mid-market companies. It turns client workflows into production agent systems the client’s own team ends up owning, using a methodology it calls TriStorm. Engagements range from four-to-eight-week diagnostics to monthly engineering retainers and longer end-to-end programs. Its delivery stack includes Pydantic AI, LangChain, and LlamaIndex with OpenTelemetry-based observability, and it maintains AgenticOS, a self-hosted open-source platform under an Apache 2.0 license that runs on Postgres, Redis, and Docker inside the client’s environment. Listed sectors include manufacturing, healthcare and regulated industries, media, and ecommerce operations. Vstorm fits a mid-market company that wants to build internal capability rather than rent it and is comfortable working with a smaller European team.
How to choose
Start with the problem, not the vendor. Write down the workflow, the systems it touches, and what “done” looks like in numbers. Then run each candidate through the same filter.
- Ask for two production references in a business like yours, and call them. Ask what broke and how fast it was fixed.
- Ask who will be on the build team by name and what their last three agent projects were.
- Ask for the architecture diagram of a recent system. Look for where the model sits and how a model swap would work.
- Ask how the agent is evaluated before release and monitored after. “We test it” is not an answer.
- Ask who owns the code, prompts, evaluation data, and connectors when the engagement ends.
- Ask what the firm will not build. A vendor who says yes to everything has not scoped anything.
Two decisions sit underneath the vendor choice. First, whether you need a custom agent at all; our comparison of custom AI agents versus out-of-the-box options walks through that. Second, whether the task is an agent problem or a deterministic automation problem, covered in our piece on AI agents versus RPA. Plenty of mid-market “agent” projects are RPA with a worse cost profile.
Frequently asked questions
What does an agentic AI development company actually do?
It designs, builds, integrates, and supports AI agents that carry out multi-step work inside your systems: scoping the workflow, choosing the model and orchestration approach, connecting the agent to your ERP, CRM, or data platform, setting guardrails and audit logs, and supporting it after launch.
How much does agentic AI development cost for a mid-market company?
Published figures vary. Deviniti lists a $25,000 minimum project size and $50 to $99 hourly rates on Clutch; larger consultancies do not publish rates. A first production agent is usually a multi-month engagement, and model, hosting, and support costs continue after launch. Get total cost of ownership in writing, not just the build fee.
How long does it take to get an agent into production?
Discovery phases at the firms above typically run four to eight weeks. A first production agent with real integrations commonly follows within a few months, depending on your data and system access. Integration readiness, not model capability, is the usual bottleneck.
Should we pick a firm that uses one cloud’s agent platform?
Only if you have already standardized on that cloud and accept its roadmap. Otherwise favor a model-agnostic architecture that lets you change models without a rebuild.
Where Xcelacore AI fits
If your company is between $50M and $1B in revenue and you need an agent that works inside the systems you already run, Xcelacore AI plans, builds, and integrates it with a model-agnostic architecture and senior engineers on the delivery team. Bring us a workflow and we will tell you whether it needs an agent.
This list reflects our opinion and is presented in no particular order. Company details were drawn from each firm’s public website, press releases, and review-site profiles at the time of writing, and may change.
