Top 15 AI Agent Development Agencies

Abstract

AI agent development agencies build systems that use AI models and connected tools to carry out business tasks. The right partner depends on the work involved:

Agency category	Best suited to
Operational and conversational	Sales workflows, customer interactions, and process automation
Custom agent development	AI-powered products, tool integrations, and multi-agent systems
Enterprise agentic AI	Complex integrations, governance, and large-scale deployment

Across all three, proven implementation experience matters more than a polished demo. Compare agencies on production evidence, reliability, technical ownership, and ongoing costs before choosing a development partner.

AI agents are quickly moving from experimental projects to practical business tools. From researching prospects and qualifying leads to managing complex workflows, they’re changing how teams work.

34% of US business leaders at companies with 10–99 employees are already piloting AI agents for workflow automation and multi-step planning across systems. The opportunity is clear, but getting useful results takes more than choosing the right technology. 

It takes a development partner who understands your processes and can turn an ambitious AI idea into something your team can actually use.

Top AI agent development agencies: Picks by category

• Operational workflows: mvpGrow specializes in HubSpot-connected AI workflows for B2B sales and RevOps. HatchWorks AI develops cross-system automation, while Netguru has documented its internal multi-agent sales assistant.
• Custom agent development: Vstorm and deepsense.ai provide useful technical documentation on orchestration, retrieval, and evaluation.
• Enterprise: EPAM has a production voice-agent reference. DataArt offers a documented enterprise AI platform, while Thoughtworks has published a governed multi-agent proof of concept.

AI agents for operational and conversational workflows

These agencies develop AI agents for sales, customer service, and internal operations. Their capabilities include lead qualification, account research, customer email handling, and workflow automation across connected business systems. 

Agency	Best suited to	Standout capabilities	Buyer consideration
mvpGrow	B2B sales and RevOps	HubSpot agents, lead qualification	GTM specialization
HatchWorks AI	Cross-system automation	GenDD, multi-agent development	Custom implementation scope
LeewayHertz	Enterprise process automation	ZBrain Builder, agent orchestration	Platform dependencies
Azilen	SaaS operations	Decision agents, predictive AI	Human approval design
Netguru	Sales and knowledge workflows	Multi-agent research, AgentOps	Internal vs. client evidence
Softblues	Customer conversations	Voice, chat, research agents	Beta vs. production maturity

1. mvpGrow

Best for: B2B companies automating marketing, sales, and revenue operations around HubSpot.

For B2B companies already using HubSpot, AI agents are most useful when they can work with the customer information and sales processes already in place. That’s where mvpGrow focuses its development work.

Its AI workflows support prospect research, lead enrichment, CRM lifecycle checks, and sales qualification. Through GTM Engineering, mvpGrow also handles the HubSpot configuration and integrations behind those workflows. This combination makes it a relevant partner for companies that want to reduce manual revenue operations work without rebuilding their existing systems.

Key capabilities

• HubSpot-connected workflows for lead and account information
• AI-assisted account research and prospect briefing
• CRM lifecycle checks and lead-routing validation
• Sales qualification and follow-up assistance
• Revenue reporting and operational summaries

Strengths and considerations: mvpGrow combines B2B go-to-market expertise with HubSpot implementation, making it particularly suited to AI workflows that depend on CRM data and established sales processes. It specializes in revenue operations rather than standalone AI software products.

Evidence and review notes: Clutch rates mvpGrow 5.0/5 from 12 reviews. Clients highlight responsive communication and dependable delivery. Salesmsg specifically credits the team with HubSpot integration work and AI SDR workflows.

2. HatchWorks AI

Best for: Companies processing high volumes of customer emails that require ERP information.

HatchWorks AI develops enterprise AI systems using its proprietary GenDD methodology. Its three core offerings are GenROI for identifying and prioritizing AI opportunities, GenDD for AI-driven development, and GenEQ for adoption and governance. Its agentic AI work includes multi-agent platforms, enterprise knowledge assistants, and workflow automation. The company also builds an email automation system integrating Outlook, NetSuite, and OpenAI.

Key capabilities

• Multi-agent orchestration and enterprise workflow automation
• AI-native product development using GenDD
• Enterprise knowledge assistants and RAG integration
• AI strategy and opportunity prioritization using GenROI
• AI adoption and governance using GenEQ

Strengths and considerations: HatchWorks combines AI strategy and adoption support. Its proprietary methodologies are delivery frameworks, not standalone agent products.

Evidence and review notes: HatchWorks has 5.0/5 from six G2 reviews. Clients value its flexibility, product expertise, and development speed. One reports nearly tripling team velocity. 

3. LeewayHertz

Best for: Enterprises building industry-specific agents through a dedicated AI platform.

LeewayHertz develops enterprise AI agents through its platform ecosystem, ZBrain. ZBrain Center of Intelligence supports use-case discovery, ZBrain XPLR assesses AI readiness, and ZBrain Builder develops and orchestrates agents. The company also describes agents for financial services, including insurance claims validation, payment disputes, and regulatory filings. 

Key capabilities

• Enterprise agent development using ZBrain Builder
• Multi-agent orchestration and workflow automation
• AI readiness assessment using ZBrain XPLR
• AI opportunity discovery through Center of Intelligence
• Industry-specific agents for finance and operations

Strengths and considerations: A defined platform ecosystem spanning planning and implementation. Confirm platform licensing, infrastructure ownership, and which industry agents have customer references.

Evidence and review notes: GoodFirms rates LeewayHertz 5.0/5 from five reviews. Customers praise its technical knowledge and reliable delivery, though some mention communication frequency and costs. 

4. Azilen Technologies

Best for: SaaS businesses developing AI-driven decision systems and operational agents.

Azilen combines product engineering with its ARC framework for agentic AI. Its published retail SaaS project uses machine-learning forecasts alongside agents that support demand planning, ordering, and inventory decisions. The implementation serves a multi-tenant retail environment and incorporates human review of recommendations. Azilen also describes AI-driven compliance workflows for financial services.

Key capabilities

• Agentic application development using ARC
• AI demand forecasting for retail operations
• Automated ordering and inventory recommendations
• Multi-tenant SaaS agent integration
• Human-governed operational decision workflows

Strengths and considerations: Specific experience combining predictive models with agent-driven decisions. Buyers should verify production status and the boundaries of agent autonomy.

Evidence and review notes: Azilen holds 4.9/5 from 13 GoodFirms reviews. Clients highlight technical expertise, responsiveness, and collaborative development, particularly when requirements change. 

5. Netguru

Best for: Companies building multi-agent assistants for sales and internal knowledge work.

Netguru offers agentic AI development services covering architecture, integration, governance, and post-launch operations. Its internal Omega assistant operates through Slack, retrieves Google Drive documents and Apollo account information, and analyzes BlueDot sales-call transcripts. Netguru uses AutoGen to coordinate specialized agents, with Langfuse and Promptfoo supporting testing and monitoring. Its published services guidance also addresses legacy-system integration and AgentOps.

Key capabilities

• Custom multi-agent orchestration using AutoGen
• Account research and sales meeting preparation
• Internal knowledge retrieval across connected systems
• Agent integration with enterprise applications
• AgentOps monitoring, testing, and governance

Strengths and considerations: Detailed engineering evidence and a defined enterprise service offering. Omega is an internal implementation.

Evidence and review notes: Netguru’s DesignRush reviews praise rapid onboarding and product design expertise. One client reports an embedded designer contributing within two weeks. 

6. Softblues

Best for: Businesses developing conversational agents and coordinated research workflows.

Softblues develops AI agents for customer communication and operational automation. Its Lumia assistant qualifies leads through web chat, WhatsApp, and voice, using language models and connected business data. The company offers Lumono, a nine-agent research pipeline in beta. Together, these projects show experience with customer-facing conversations and specialized agents working on a shared research process.

Key capabilities

• Multichannel conversational agent development
• AI voice assistants for lead qualification
• Knowledge-grounded responses using vector search
• Lead capture and business-system integration
• Multi-agent research automation through Lumono

Strengths and considerations: Named projects and identifiable technologies. Confirm deployment status and which features have production customer evidence.

Evidence and review notes: Softblues scores 5.0/5 from four GoodFirms reviews. Clients appreciate responsive support, attention to detail, and dependable long-term collaboration. 

Custom AI agents and product development

These agencies develop custom AI agents for software products, internal platforms, and specialized business applications. They offer multi-agent orchestration, knowledge retrieval, external tool integration, and production deployment. They are especially relevant to companies that need tailored agent architecture, testing, and infrastructure rather than prebuilt automation tools.

Agency	Best suited to	Standout capabilities	Buyer consideration
Vstorm	Production-ready custom agents	LangGraph, PydanticAI, deployment	Infrastructure handover
deepsense.ai	Complex agent architectures	ragbits, RAG, MCP	Framework maintenance
Miquido	AI-powered digital products	Custom agents, app integration	Evaluation approach
Neoteric	Specialized business software	Knowledge retrieval, AI assistants	Agent autonomy
NineTwoThree	Custom enterprise agents	Multi-agent, SQL agents, MCP	Evaluation and ownership

7. Vstorm

Best for: Companies developing custom agents with production infrastructure and technical ownership.

Vstorm specializes in custom AI agent development, including multi-agent systems, knowledge retrieval, evaluation, and deployment. Its engineering approach uses PydanticAI and LangGraph for agent behavior, alongside observability and evaluation tools. Vstorm also publishes dedicated infrastructure guidance covering containerization, deployment, and operational handover. 

Key capabilities

• Custom agents with structured outputs using PydanticAI
• Multi-agent workflows using LangGraph
• Knowledge retrieval through vector databases
• Agent evaluation and execution tracing
• Production deployment and infrastructure handover

Strengths and considerations: Clear focus on agent engineering and deployment. Its custom-build approach makes the most sense for companies investing in their own AI infrastructure.

Evidence and review notes: GoodFirms rates Vstorm 4.8/5 from five reviews. Customers praise its problem-solving skills, organized delivery, and regular progress updates. 

8. deepsense.ai

Best for: Engineering teams building agents that need to work with external tools and complex data.

One of deepsense.ai’s most interesting contributions is ragbits, its open-source framework for generative AI development. With ragbits, developers can give agents access to Python functions, connect external tools through MCP, and coordinate specialized agents. The documentation even walks through a travel-planning example where different agents handle flight searches and destination research. OpenTelemetry tracing helps developers see what happened during execution.

Key capabilities

• Custom AI agent development
• Multi-agent orchestration and communication
• MCP and Python tool integrations
• RAG pipelines and knowledge retrieval
• Execution tracing with OpenTelemetry

Strengths and considerations: The open-source framework offers technical flexibility and transparency. Public customer deployment examples are less detailed than its engineering documentation.

Evidence and review notes: Clutch reviews of deepsense.ai highlight strong engineering skills and effective handling of complex technical projects. Some clients mention needing clearer initial alignment. 

9. Miquido

Best for: Companies building AI agents into mobile apps, web platforms, and business software.

Miquido develops agents that fit into applications and workflows people already use. Its team builds custom agents and multi-agent systems, plus integrations with tools such as n8n and Make. Miquido also offers a four-week AI Kickstarter for Agents, giving companies a defined development engagement before a larger rollout. Its broader AI services include RAG, vector databases, and LLM integration.

Key capabilities

• Custom AI agents and multi-agent systems
• AI features for mobile and web applications
• Workflow automation with n8n and Make
• RAG and LLM integration
• AI Kickstarter for Agents

Strengths and considerations: Combines AI development with experience delivering digital products. Its public case studies show more evidence of AI-powered applications than autonomous agent systems.

Evidence and review notes: Miquido’s DesignRush profile includes positive feedback on the team’s talent and work ethic, although detailed client reviews are limited. 

10. Neoteric

Best for: Companies building AI-powered software products that need custom development and integration.

Neoteric works with companies that want to add AI to their software or develop a new product around it. Its engineers handle the work from early technical discovery through development and maintenance, including Gen AI and machine learning applications. The team has experience using GPT models alongside LangChain, Pinecone, and Chroma. The company also offers AI MVP development, helping businesses test a product idea before investing in a larger implementation.

Key capabilities

• Custom AI application and product development
• LLM integration with existing software
• Semantic search and knowledge retrieval
• AI-powered text analysis and recommendations
• AI MVP development and maintenance

Strengths and considerations: Neoteric has solid experience integrating AI into software products. Its public work provides less evidence that agents can independently execute complex, multi-step workflows.

Evidence and review notes: Neoteric’s Clutch reviews praise technical expertise, flexibility, and project delivery. Some feedback raises concerns about AI research capabilities in individual engagements. 

11. NineTwoThree

Best for: Businesses developing custom conversational agents and AI-powered applications.

NineTwoThree AI Studio builds AI agents that execute business workflows, process documents, query databases, and interact with enterprise applications. Its services cover autonomous workflow agents, conversational AI, document intelligence, and multi-agent orchestration. The company integrates agents with Salesforce, HubSpot, Slack, SAP, and custom databases using APIs and Model Context Protocol (MCP). It also provides role-based access controls, human approval workflows, and a 27-point evaluation suite.

Key capabilities

• Workflow automation and multi-agent orchestration
• Document intelligence and SQL agents
• Conversational AI with knowledge retrieval
• Enterprise integrations using APIs and MCP
• Agent testing and human approval controls

Strengths and considerations: A good fit for bespoke agents that need substantial integration work. Confirm ongoing support arrangements and maintenance costs.

Evidence and review notes: NineTwoThree holds 4.9/5 from 42 Clutch reviews. Clients praise its AI expertise, transparent communication, and delivery quality, with occasional requests for deeper initial discovery. 

Enterprise agentic AI and complex integrations

These agents address projects where security, access controls, existing infrastructure, and operational scale are central requirements. 

Agency	Best suited to	Standout capabilities	Buyer consideration
DataArt	Enterprise AI infrastructure	Secure gateways, knowledge systems	Agent execution scope
Thoughtworks	Governed AI applications	Multi-agent systems, Bedrock	Pilot-to-production path
EPAM	Large-scale agent deployment	DIAL, AI/Run, voice agents	Platform operating costs
Sphere	Internal operations	AI Foundry, workflow agents	Production evidence

12. DataArt

Best for: Large enterprises building governed AI platforms and applications connected to internal systems.

DataArt combines enterprise software engineering with AI platform development. The platform uses a controlled model-routing layer, conversation memory management, and enterprise data connections. The tool reports more than 3,500 daily active users shortly after launch.

Key capabilities

• Enterprise AI platform architecture and development
• Secure internal knowledge retrieval
• Centralized AI model access and routing
• AI application integration with enterprise systems
• Conversation memory management and audit controls

Strengths and considerations: Strong enterprise integration and governance evidence. The reference describes a platform, not unrestricted autonomous workflows.

Evidence and review notes: DataArt has 5.0/5 from five GoodFirms reviews. Clients value its engineering talent, business understanding, and ability to work alongside internal teams. 

13. Thoughtworks

Best for: Enterprises developing agentic systems with strong governance and human oversight.

Thoughtworks combines AI engineering with enterprise transformation and responsible AI practices. Its Splio project demonstrates a multi-agent marketing workbench built with Amazon Bedrock and the Strands framework. The agents interpret campaign requests, gather context, generate materials, and apply brand controls before human approval. 

Key capabilities

• Enterprise multi-agent application development
• Agent orchestration using Amazon Bedrock
• AI-assisted campaign planning and content generation
• Brand and policy controls for agent outputs
• Human approval within agent workflows

Strengths and considerations: Strong emphasis on governance and enterprise delivery. The Splio project demonstrates feasibility, not a production rollout.

Evidence and review notes: Gartner Peer Insights rates Thoughtworks 4.6/5 from 92 ratings. Customers praise its engineering expertise and collaboration, although some report inconsistent team quality. 

14. EPAM Systems

Best for: Enterprises deploying agentic AI across customer service and complex technology environments.

EPAM offers enterprise AI development through its AI/Run ecosystem. Its products include DIAL, an open-source platform for AI orchestration and governance, and migVisor, an agentic platform for data modernization. It also has a substantial customer-service deployment with telecommunications provider 1&1, and reports more than 20 AI agents handling over 100,000 calls weekly. 

Key capabilities

• Enterprise AI orchestration using DIAL
• Agent development through the AI/Run platform
• Agentic data modernization using migVisor
• High-volume customer-service voice agents
• Enterprise AI governance and authentication controls

Strengths and considerations: Combines named AI platforms with evidence from production customer-service use. Confirm which EPAM products underpin the proposed implementation.

Evidence and review notes: EPAM holds 4.9/5 from 88 Gartner Peer Insights ratings. Clients highlight technical expertise, dependable delivery, and responsive account management. 

15. Sphere Partners

Best for: Businesses integrating AI into internal operations and enterprise applications.

Sphere Partners provides AI engineering and business-process automation services. Its published Sphere AI Foundry project includes internal agents for contact research, meeting preparation, and activity summaries, with approval controls and audit logging. It has also documented network operations automation that classifies incidents, supports assignment, and retrieves historical resolutions. 

Key capabilities

• Internal AI agents for contact research
• AI-assisted meeting preparation and activity summaries
• Agent workflows with approval and audit controls
• Automated incident classification and assignment
• Historical incident knowledge retrieval

Strengths and considerations: Relevant internal AI and operational automation experience. Confirm which capabilities are autonomous agents and which use conventional automation.

Evidence and review notes: Sphere has 4.9/5 from 32 Clutch reviews. Customers praise its engineering quality and responsiveness, while some mention communication challenges and higher costs. 

How we selected these agencies

We assessed published customer projects, technical documentation, implementation details, and available independent feedback. We prioritized evidence of actual agent behavior, connected business systems, evaluation methods, and deployment maturity. A production customer implementation carries different weight from an internal tool or proof of concept.

The list is not a scored ranking. We did not conduct hands-on testing or independently benchmark the agencies. We did not treat general software-development reviews as agent-specific endorsements.

How to choose an AI agent development agency

• Find a team that knows your type of project: An agency might be excellent at building customer-facing chat agents but have little experience with complex internal workflows. Look at what it has actually delivered, which systems it works with, and whether its expertise matches the job you have in mind.
• Look past the impressive demos: A polished demo can hide a lot. Can the agent complete tasks across different systems, or does it only answer questions? Has it been deployed for customers? Ask how the agency tests accuracy, tracks agent actions, and handles failures. For important decisions, employees should be able to intervene or take over.
• Know what you’re paying for: Give two or three shortlisted agencies the same pilot brief, with clear deliverables and measurable success criteria. Find out who owns the code, data, and evaluation tools, and what you’ll receive at handover. Confirm whether the price includes hosting, model usage, third-party licenses, monitoring, and ongoing support. Also ask who handles model updates and what happens if you need to switch off or replace the agent.

AI agents for B2B marketing, sales, and RevOps with mvpGrow

A sales rep preparing for a call needs accurate account information, not another tool to manage. And when leads move between marketing and sales, the CRM needs to reflect what’s actually happening.

mvpGrow develops AI workflows around these everyday revenue operations tasks. Agents can help prepare account briefs, enrich lead records, identify lifecycle inconsistencies, and support qualification using information already held in HubSpot.

The underlying CRM setup matters just as much as the agent. Through GTM Engineering, mvpGrow connects the systems and configures the workflows needed to keep information moving between teams. For B2B companies, this means starting with a specific revenue-process problem and building automation around the tools employees already use.

Frequently asked questions

What does an AI agent development agency do?

It designs and builds systems that use AI models and connected tools to complete defined tasks. Work can include architecture, integrations, testing, deployment, and maintenance.

How much does AI agent development cost?

Costs depend on complexity, integrations, security, and ongoing support. Most agencies provide custom quotes. Request separate estimates for discovery, pilot development, production rollout, and maintenance.

How long does development take?

A narrow proof of concept may take weeks. A production system involving several business applications and security requirements can take months.

How do AI agents differ from traditional automation?

Traditional automation follows predefined rules. AI agents can interpret information and select actions within defined permissions. Many reliable implementations combine the two.

Should I hire an agency or build in-house?

An agency provides specialist engineering capacity. An internal team offers direct ownership but needs resources to maintain and evaluate the system. Some companies commission the initial build and then bring operations in-house.

Turn AI agent potential into measurable results

The right AI agent development agency depends on what you need the agent to do. A custom software product requires different expertise than an internal workflow connected to your CRM.

For B2B companies, some of the most useful opportunities are already sitting inside existing sales processes: account research that takes too long, inconsistent lead qualification, or information that doesn’t reach the right team. mvpGrow combines GTM strategy with AI implementation to build these workflows around your existing revenue systems.

Ready to put AI to work in your revenue process? Talk to mvpGrow about the marketing or sales workflow you want to improve.