Top 16 AI Agent Development Companies in 2025

AI agents are already solving real problems, handling product enrichment, support tickets, forecasting, and internal workflows. And while chatbots are what most people still associate with AI agents, the reality is different. A chatbot answers questions. An AI agent gets things done: inside your systems and across your data.

That’s why more companies are now turning to AI agents to reduce manual work, speed up decision-making, and scale operations without growing headcount. But where do you start?

In this guide, we’ll give you a vetted list of 16 companies that know how to build and implement real AI agents. You’ll also find practical examples across eCommerce, sales, logistics, HR, and analytics, so you can see exactly where AI agents are already making an impact.

Key takeaways

• An AI agent is software that acts toward a goal; it uses reasoning, memory, and tools to complete tasks inside your systems, not just respond with text.
• To get the most out of AI agents, start with business problems, not tools: ask for a pilot tied to one KPI, then scale what works.
• Favor agencies that ship integrated workflows across ERP, CRM, PIM, and data warehouses, not just chat UIs.

What is an AI agent?

An AI agent is software that pursues a goal and takes actions to achieve it. It reads context, plans the next step, calls tools or APIs, updates systems, and checks the result against a target. Think of it as an autonomous layer that turns instructions and data into completed tasks.

Unlike a static chatbot that only replies, an agent can work inside your stack. It can check stock, create a draft purchase order, enrich a product record, or route a support ticket with a suggested resolution. In eCommerce, this might mean turning a shopper’s idea into a product list, checking a product feed before launch, handling returns, or flagging something unusual in your daily sales.

Every reliable agent has a few common parts. Inputs can be text, files, events, or database rows. Reasoning and policies decide what to do next. Memory keeps useful context, so the agent does not repeat itself. Tool access lets it act in ERPs, CRMs, PIMs, WMS, or analytics. Monitoring records what happened, and handoff rules send edge cases to humans.

What matters most is that well-designed agents are built around a clear KPI, not just clever prompts. They must record what they do, and be regularly tested to see how well they hit their goal. Tracking things like accuracy, speed, and cost helps teams stay focused on business impact and makes the agent safe to scale.

Best AI agent development agencies and companies

We reviewed dozens of agencies across the US and Europe and selected those that go beyond chatbots to deliver real AI agents: tools that take action inside live business systems. We focused on those with proven results, not just frameworks, and highlighted their strengths by use case and industry.

Also read:
Best AEO Agencies to Help You Get Mentioned in AI-Powered Results

1. scandiweb: Custom eCommerce agents for measurable ROI

scandiweb has spent 22+ years building and optimizing large-scale eCommerce ecosystems, delivering 2,100+ projects with 600+ in-house specialists and the world’s most certified Adobe Commerce and Hyvä teams. That depth matters when your agent must talk to Magento or Shopify, or any other eCommerce platform, besides respecting catalog logic, and updating analytics cleanly.

What they build:

• AI agents for merchandising, content, feed QA, support triage, analytics summaries, and AEO
• Multi-agent workflows that coordinate product data, marketing ops, and BI dashboards.
• Analytics copilots that summarize GA data, surface anomalies, and recommend actions.

Best for: If you need agents that live inside eCommerce processes (catalog, PDP/PLP, checkout, PIM, OMS), plus the analytics to prove ROI.

2. STX Next: Python-first agent engineering for data-heavy use cases

STX Next builds agents with a Python core and a strong data spine, then wires them into your internal systems. Their team emphasizes human-in-the-loop controls, dashboards, and gated rollouts, which suits regulated workflows. Typical projects include agents that fetch from internal APIs, process files, and post results back into CRMs, ERPs, or data warehouses. If you need a partner that treats the agent as a production microservice with observability rather than a demo bot, this is a solid fit.

Best for: Enterprises that want senior Python talent to connect agents to internal data sources and APIs.

3. 10Clouds: LLM products with RAG and vector search

10Clouds delivers retrieval-grounded assistants that draw on private knowledge bases, with LangChain and vector databases as standard building blocks. They focus on secure deployments, cloud readiness, and the practical details of permissions, tool calls, and evaluation so the agent stays on-task and auditable. Their work spans knowledge copilots, automation bots that call business tools, and production RAG systems.

Best for: Research assistants, knowledge copilots, and task agents that depend on accurate retrieval.

4. Neoteric: Conversation and decision agents for growth teams

Neoteric ships conversational agents that plug into marketing, sales, and support stacks, then tunes them on brand data and product context. Expect careful attention to prompt strategy, fine-tuning, and analytics hooks so teams can see what the agent answered and why. They also support broader AI builds when the use case needs prediction or classification alongside chat.

Best for: Support, pre-sales, and marketing assistants tied to CRM and CDP data.

5. Vstorm: Agentic automation for ops and BI

Vstorm focuses on multi-agent workflows that move data across tools, close loops, and report outcomes. Their consultants bring MLOps habits to agent projects, which helps with testing, rollout, and ongoing evaluation. If your current process bounces between sheets, tickets, and dashboards, they will model each step as a tool-using agent and orchestrate the flow.

Best for: Replacing manual hand-offs across spreadsheets, tickets, and dashboards.

6. MindTitan: Applied NLP and computer vision

MindTitan builds practical AI for the public and private sectors, with experience in NLP, CV, and robust data pipelines. Their work often targets service operations where quality of service and traceability matter, such as telecom and government use cases. They pair model work with the plumbing that feeds and monitors it.

Best for: Ticket deflection, routing, and classification with measured QoS.

7. Eleks: Enterprise AI with governance and integration depth

Eleks approaches agents as part of a larger transformation: data platforms, security, and integration patterns sit alongside the agent logic. They support agentic systems across finance, healthcare, energy, and manufacturing, and provide the governance scaffolding enterprises expect. If you need a partner that can manage multi-system rollouts with documentation and change control, add them to the shortlist.

Best for: Enterprises that need strong PMO, documentation, and compliance.

8. DataRoot Labs: R&D-grade LLM and multimodal builds

DataRoot Labs takes on higher-ambiguity work, including custom LLMs, RAG pipelines, and multimodal agents. They design evaluation harnesses so teams can measure progress and failure modes rather than rely on gut feel. When the problem requires research rigor and fast prototyping, their model-plus-engineering approach helps de-risk the path to production.

Best for: High-ambiguity projects and prototypes that need research rigor.

9. Systango: Analytics-led ML and anomaly detection agents

Systango builds agents that sit on top of predictive and anomaly-detection models to support finance, marketplace, and ops decisions. They combine data engineering with ML so agents can pull fresh signals, score risk, and trigger actions or human reviews. This is useful when outcomes must be tied to quantified thresholds rather than open-ended chat.

Best for: Finance, marketplaces, and ops teams that need data-first agents.

10. ITRex: Broad agent catalog and system design

ITRex offers a full catalog of agent types, from rule-based and goal-driven to learning agents, and handles the lifecycle from strategy to support. They are comfortable integrating with enterprise stacks and setting up the monitoring and guardrails needed for long-running automations. If you want one vendor to cover several departments, their scope fits.

Best for: Firms seeking a single partner for multiple agent types across departments.

11. HatchWorks: Strategy to shipped agents for product teams

HatchWorks pairs product strategy with delivery using a method they call Generative-Driven Development. Agents and autonomous workflows are woven into sprints, with clear ROI checkpoints and governance baked in. This suits product organizations that want working software on a predictable cadence, not a one-off experiment.

Best for: Product organizations that need a pragmatic path from idea to pilot to scale.

12. LeewayHertz: Build-to-spec agents using popular frameworks

LeewayHertz assembles task-focused agents with tools like AutoGen Studio, Vertex AI Agent Builder, and crewAI, then connects them to enterprise systems. Their value is speed: they map the use case to a known pattern and stand it up with logging, testing, and versioning so teams can iterate safely.

Best for: Rapid sprints to stand up specialized agents with known tools.

13. Markovate: Compact team for LLM agents and voice interface

Markovate develops LLM agents alongside voice interfaces for call handling, order capture, and field workflows. They can deliver a chat or voice layer and the integrations needed to fetch data, update records, and confirm actions in real time. That mix is useful when users are on the move or when phone traffic remains high.

Best for: Support and field workflows where voice matters.

14. ML6: Applied AI consultancy

ML6 is a European consultancy with deep data engineering and MLOps capabilities. They help teams move from models to reliable services and partner closely with major cloud providers. If your agents need strong pipelines, monitoring, and cost control on cloud, they bring the required discipline.

Best for: Enterprises that need reliable delivery and governance.

15. Xomnia: Data engineering and AI

Xomnia combines data engineering with agent builds so teams can stand up agents while modernizing ELT and monitoring. They frame the agent as a data product with iterative delivery and shared metrics, which helps cross-functional adoption. This approach suits organizations that need to fix data foundations while they ship value.

Best for: Organizations that must fix data foundations while rolling out agents.

16. Intercom: Customer service agents built for real-time support

Intercom focuses on AI agents that go beyond simple chatbots. Instead of just answering questions, these agents resolve issues, suggest resources, and escalate to humans when needed. They integrate with CRMs and help desks so every interaction is tied to customer history. For teams under pressure to shorten response times and reduce drop-offs, Intercom’s agents provide consistent, always-available support across chat, email, and in-app messaging.

Best for: Companies that want customer service agents to deliver instant, context-aware support across multiple channels.

How AI agents deliver real impact across different business functions

AI agents are not limited to answering questions. When connected to the right data and tools, they can act, adapt, and support multiple parts of your operation, without needing a human to hand-hold every step. Below are practical examples of how AI agents are already helping teams get more done with less manual work.

Product information and content

Product data agent

Keeping catalogs accurate is a never-ending task. A product data agent reduces that burden by validating specs, spotting missing fields, correcting inconsistencies, and flagging outliers. Because it pulls from ERP or supplier feeds and syncs with a PIM or eCommerce platform, it’s particularly useful for retailers with large or frequently updated assortments.

AI agents can even turn email into a knowledge base, making internal communications searchable and actionable.

Content enrichment assistant

On the customer-facing side, a content enrichment assistant generates or improves titles, descriptions, feature lists, and comparison blocks. When tied to AI + PIM workflows, it also helps localize content, spin out product variants, and ensure the messaging is both brand-aligned and SEO-ready.

Merchandising and category management

Merchandising agent

Merchandising teams don’t have to guess which rules will improve performance. A merchandising agent tracks PLP data, margins, and stock levels, then proposes adjustments such as sort rules, filter tweaks, or new groupings. Over time, it can test its own logic against actual results and refine recommendations further.

Promotion optimizer

Campaigns also benefit. A promotion optimizer detects issues like overlapping discounts or broken promo logic and then recommends corrections. By considering seasonality, sales history, and current inventory, it ensures promotions add value instead of eroding profit.

Customer service and support

Support copilot

Support teams often face a flood of repetitive queries. Here, a support copilot provides relief by pulling context from past tickets, suggesting help articles, routing complex issues, and drafting responses for agents to approve. The result is faster handling without losing the human touch.

See a practical customer service agent walkthrough that shows how agents handle FAQs and escalate complex cases.

Returns and complaints handling

Returns and complaints are another high-volume area. AI agents can review incoming tickets, categorize them by type and urgency, check order histories, and automatically start refund or replacement workflows. Some systems also go a step further by turning email exchanges into a searchable knowledge base, making it easier for support teams to find answers and act quickly.

Sales and personalization

Onsite personalization agent

Personalization is no longer optional. An onsite personalization agent adjusts product recommendations, banners, and messaging in real time, drawing on CRM or CDP data to match each visitor’s intent.

Cart abandonment recovery agent

Meanwhile, a cart abandonment recovery agent keeps potential buyers engaged. It watches for exit signals, then follows up with tailored nudges via chat or email. Sometimes it escalates to a human when a purchase looks especially likely.

Explore how HubSpot form enrichment with AI improves lead quality and saves sales teams time.

Analytics and performance monitoring

Analytics copilot

Staying on top of performance requires constant vigilance. An analytics copilot scans sales, traffic, and conversion rates for spikes or drops, then summarizes anomalies in plain language and suggests next steps. This alone can save hours of manual analysis every week.

Forecasting assistant

Looking further ahead, a forecasting assistant studies historical patterns in sales, returns, and customer behavior. Its projections inform inventory planning, marketing budgets, and staffing decisions, helping businesses prepare instead of react.

Operations and fulfillment

Supply and fulfillment agent

Logistics present their own challenges. A supply and fulfillment agent monitors carrier SLAs, stock levels, and delivery times, flagging delays or shortages and recommending workarounds such as alternate routes or split shipments.

Order flow monitor

Finally, an order flow monitor ensures nothing gets stuck. By checking statuses in real time and catching errors early, it can trigger alerts or fallback processes that keep operations running smoothly and customers satisfied.

Case study: How a Nordic DIY retailer scaled project guidance with a 24/7 sales assistant

A leading home-improvement chain in Sweden, Norway, Finland, and Denmark runs 200+ stores and exceeds $1B in annual revenue. Roughly 70% of annual revenue is concentrated between May and September, and customers come in with project ideas but no product lists. Staff expands by up to 10x, mostly temporary workers, and training is too slow and inconsistent.

In collaboration with Algoritma, we built a web-based ShopBot trained on common DIY projects and connected to the full product catalog. The assistant can advise on projects step by step and recommend SKUs, sizes, and quantities. Acting as a form of conversational commerce, it guides shoppers, generates a clear plan for customers, and supports store teams with consistent answers.

Also read:
How AEO Helped Enviropack Become a Top AI Pick for Sustainable Packaging

How to pick the best partner for AI agent development

Not every agency that builds with LLMs can deliver a reliable, goal-driven AI agent. The difference comes down to whether they treat your project as a production system or a demo. Choosing the right partner means asking the right questions—not just about model choice, but about systems, measurement, and long-term fit.

The strongest vendors will build around your KPI, understand how to work with your stack, and deliver something your team can trust, own, and improve over time. Use this checklist to compare vendors objectively and keep the focus on outcomes.

What to look for

Score each vendor from 1 to 5 on the items below.

• Business impact: Are success metrics clearly defined? Do they track accuracy, latency, cost per task, or similar KPIs?
• Data foundation: Can they access and work with data from your PIM, ERP, CRM, WMS, or analytics stack?
• Agent design: Do they explain how the agent handles memory, tool use, planning, fallbacks, and safety checks?
• Integration depth: Are webhooks, queues, API rate limits, and error handling built into the plan?
• Operations: Can they support CI/CD, secrets management, observability, and rollback? Is there a human-in-the-loop for edge cases?
• Governance and compliance: Do they have a plan for handling sensitive data (PII), red-teaming, approvals, and audit trails?
• Post-launch support: Can they monitor drift, fine-tune agents over time, and manage hosting costs?

Before you commit, ask every vendor to provide the following:

• A 3-week pilot with one clear KPI and a target success rate
• A handover pack that includes prompts, architecture diagrams, datasets, and runbooks
• An evaluation harness with example tasks, success thresholds, and test coverage
• A review process where a human approves outputs until performance is proven.

Expected future trends in AI agent development (2026–2027)

AI agents are maturing quickly. What started with single-task bots is evolving into systems that can plan, adapt, and collaborate across workflows. As adoption grows, we’re seeing clear patterns emerge around how agents are built, deployed, and governed. Here’s what AI experts are predicting for the future.

Rise of multi-agent systems

AI is moving from single-task assistants toward multi-agent systems that collaborate, critique each other, and divide complex goals into smaller steps. Forrester highlights this as part of the broader “AI agent pivot,” noting that real autonomy in production is still rare, but adoption is accelerating as frameworks mature. These systems are particularly valuable in scenarios that require planning, tool coordination, and oversight across multiple workflows.

RAG, self-hosting, and evaluation as the default stack

Retrieval-Augmented Generation (RAG) is becoming the baseline for grounded, context-aware agents. At the same time, many enterprises are turning to self-hosted or sovereign deployments to maintain data privacy, reduce latency, and control costs. This shift drives demand for evaluation and monitoring platforms such as HoneyHive, which help teams measure accuracy, safety, and cost-effectiveness before scaling agents.

Emphasis on safety, transparency, and regulation

Governance is no longer optional. The EU AI Act has entered force with obligations rolling out through 2026, while frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 provide structured approaches to transparency, safety, and lifecycle control. Vendors that ship agents with audit trails, permission scoping, and measurable evaluation policies will face fewer roadblocks during compliance reviews.

Security as a new proving ground

Cybersecurity is becoming a key domain for AI agents. Startups like Qevlar are developing autonomous responders and analyst copilots, attracting strong investor interest. While Forrester notes that adoption in security operations is still in early stages, the potential is significant – especially for threat detection, incident triage, and automated response under human oversight.

Final thoughts

AI agents are already working behind the scenes: fixing product data, routing support tickets, flagging issues before they become problems. What’s changing is how well they’re integrated, how reliable they are, and how fast companies are putting them into production.

If you’re here, you’re not looking for another chatbot. You’re looking for something that solves real problems, connects with your systems, and supports your KPIs.

Start with one use case. Tie it to one metric. Scale what works.

And if you want a second opinion or need help getting started, we’re happy to share what we’ve learned building agents that actually ship.

Want help building your first AI agent? Join our AI Accelerator to define the use case, align on KPIs, and build a working agent in weeks. Includes workshop, roadmap, and up to 30% co-funding.

Frequently Asked Questions

What is a “real” AI agent?

Software that can plan, call tools or APIs, keep context, and act toward a goal with measurable outcomes.

How long does an agent take to ship?

With a clear KPI and existing APIs, a narrow pilot can go live in a few weeks. Broader, multi-system agents take longer once data work and governance are included.

What should I budget?

Expect an initial pilot in the tens of thousands, then ongoing spend for hosting, evaluation, and tuning. Data work often drives the timeline and cost.

How do we keep agents safe?

Use scoped permissions, rate limits, tests, red-teaming, and a human-review loop. Track failure modes and maintain an evaluation harness.

Will agents replace teams?

They reduce repetitive tasks and speed decisions. The best outcomes happen when people handle exceptions and higher-value work.

About scandiweb

scandiweb is a full-service eCommerce agency helping brands build, scale, and optimize across platforms like Adobe Commerce, Shopify, BigCommerce, and commercetools. We design and implement AI agents that work inside real systems, supporting merchandising, support, analytics, and ops with measurable results.

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