Top 12 Agentic AI Development Companies in 2026

Most companies experimenting with agentic AI are still focused on what agents can do in isolation. Generate responses. Complete tasks. Automate fragments of workflows.

But enterprise value rarely comes from isolated capability. It comes from how well these agents operate inside real systems where data is fragmented, decisions carry risk, and every action needs to be traceable.

This is where most implementations start to break.

Research from Gartner shows that a large share of AI initiatives fail to move beyond pilot stages, largely due to integration, governance, and operational challenges. In simple terms, the bottleneck is not intelligence. It is execution.

As a result, choosing the right agentic AI development partner has become a critical decision. Teams need partners who can go beyond prototypes and design multi-agent systems that integrate with enterprise data, operate within governed environments, and scale without losing control over cost or outcomes. This requires a combination of architectural depth, platform expertise on ecosystems like Microsoft Azure, and the ability to embed agents into real business workflows.

We have compiled this verified list of the top agentic AI development companies to help you evaluate partners who not just build AI agents, but make them reliably work inside complex real-world environments.

Most vendor lists tend to reward visibility or breadth of services. That approach does not work for agentic AI. The gap between a compelling demo and a system that holds up in production is significant, and that gap shaped how we evaluated companies.

1. Evidence of Real Deployments

We looked for signs that companies have moved beyond pilots. This includes case studies, long-running engagements, and systems that are actively used in business workflows, not just showcased in controlled environments.

2. Ability to Handle Ambiguity

Agentic systems are tested most in edge cases, not happy paths. We prioritized companies that demonstrate the ability to design systems that can deal with incomplete data, conflicting inputs, and exception-heavy workflows.

3. Architectural Thinking, Not Just Implementation

We evaluated whether firms approach agentic AI as part of a broader system design problem. This includes how agents fit into data flows, decision layers, and existing applications rather than being treated as standalone features.

4. Control After Deployment

A key differentiator was how companies think about what happens after go-live. Can teams monitor behavior, intervene when needed, and continuously refine outcomes without rebuilding the system?

5. Consistency Across Engagements

Instead of one-off success stories, we looked for patterns. Companies that repeatedly deliver similar outcomes across industries signal a more mature and repeatable approach.

6. Practical Fit for Enterprise Environments

Finally, we considered whether these companies can operate within the constraints most teams face. Budget limits, legacy systems, security requirements, and evolving business needs all play a role in whether agentic AI actually works.

With unmatched scale across strategy, consulting, technology, and operations, Accenture is one of the most comprehensive agentic AI partners for global enterprises. Its AI Refinery platform, created by Accenture and built on NVIDIA AI Foundry, NVIDIA AI Enterprise, and NVIDIA Omniverse, helps companies turn raw AI technology into useful business solutions. Having supported more than 2,000 generative AI reinvention projects for organizations across industries, Accenture launched AI Refinery™. This includes a collection of 12 industry agent solutions to help organizations rapidly build and deploy a network of AI agents that can enhance its workforce, address domain-specific challenges, and drive business value faster.

Quick Facts

• Founded: 1989
• Headquarters: Dublin, Ireland (operations in more than 120 countries)
• Team size: 700,000+ employees
• Core AI capabilities: Agentic AI design and deployment, multi-agent orchestration, AI Refinery platform (Agents, Knowledge, Models, Governance), industry-specific agent solutions, physical AI, generative AI.
• Key technologies: NVIDIA AI Enterprise software, including NVIDIA NeMo, NVIDIA NIM microservices, and NVIDIA AI Blueprints, OpenAI enterprise models and AgentKit; Microsoft Azure OpenAI; Google Cloud Vertex AI and Gemini models; Anthropic Claude.
• Industry focus: Consumer goods and services, life sciences, industrial/manufacturing, B2B marketing; and across financial services, healthcare, public sector, and retail.
• Certifications and credentials: Microsoft Solutions Partner across Data & AI, Digital & App Innovation, Infrastructure, and Security, with advanced Azure and AI specializations. Strategic Microsoft partner for Azure and Azure OpenAI deployments, supported by enterprise-grade governance and global ISO-certified delivery standards.
• Minimum project size: Enterprise-scale programs
• Engagement models: Consulting-led transformation, managed services, co-innovation programs
• Notable clients: Global Fortune 500 enterprises across banking, retail, telecom, life sciences, and government.
• Website:com

Why they stand out

• Proprietary agentic AI platform built for enterprise scale: AI Refinery includes Agent Builder, Trusted Agent Huddle™, and SDKs that help companies quickly deploy and safely orchestrate agentic AI across the enterprise ecosystem.
• Industrialized agent development through the Distiller framework: The AI Refinery distiller agentic framework encapsulates what is needed across the end-to-end agent lifecycle — including agent memory management, multi-agent collaboration, agentic workflow management, model customization and evaluation, governance and observability, and cross-platform interoperability.
• Deep hyperscaler AI ecosystem: A first-of-its-kind platform for secure and seamless AI agent collaboration across partners like Adobe, AWS, Google Cloud, Microsoft, and others enables organizations to optimize agent performance for specific tasks. Accenture is also working toward developing industry-specific AI agent solutions.

As one of the world’s oldest technology institutions, IBM brings an unmatched combination of enterprise software, open-source innovation, and research depth to agentic AI. For IBM, the evolution toward agentic AI is embodied in BeeAI and Agent Stack — two open-source initiatives that have reshaped how enterprises build and deploy intelligent agents. At the centre of IBM’s commercial agentic AI offering is the Watsonx platform, a unified suite spanning AI development, agent orchestration, and governance, purpose-built for enterprises that need to scale AI responsibly, without vendor lock-in.

Quick facts

• Founded: 1911
• Headquarters: Armonk, New York, USA
• Team size: 280,000+ employees (LinkedIn)
• Core AI capabilities: Agentic AI design and orchestration, multi-agent workflows, AI governance, open-source agent frameworks (BeeAI, Agent Stack), mainframe AI modernization, hybrid cloud AI deployment.
• Key technologies:ai, Watsonx.data, Watsonx.governance, IBM Granite models, Red Hat OpenShift AI.
• Industry focus: Financial services, customer service, supply chain optimization, cybersecurity, and responsible AI governance; and across healthcare, telecommunications, retail, and public sector.
• Certifications and recognitions: Named a Leader in the 2025 Gartner® Magic Quadrant™ for AI Application Development Platforms, IDC MarketScape named IBM a Leader in 2025 generative AI evaluation technology , IBM watsonx Orchestrate recognized with 2025 iF and Red Dot design awards.
• Minimum project size: Enterprise-scale programs
• Engagement models: Standalone product subscriptions, enterprise platform deployments, co-creation via Watsonx AI Labs, IBM Consulting AI services, managed services.
• Notable clients: UFC, Scuderia Ferrari HP, and Dun & Bradstreet alongside Fortune 500 enterprises across financial services, telecom, and manufacturing.
• Website: www.ibm.com

Why the stand out

• A dedicated innovation hub accelerating real-world agentic AI: IBM launched Watsonx AI Labs, a developer-first innovation hub in New York City designed to co-create domain-specific AI solutions for enterprises’ most complex challenges.
• Enterprise-grade governance built into every layer: IBM governs AI agents in a security-rich environment with centralized oversight, built-in guardrails, and automated policy enforcement.
• A complete, open agentic AI platform with no lock-in: IBM Watsonx Orchestrate is built to be open, integrated, trusted, and hybrid, bringing the power of agentic AI to current workflows, automations, and apps with no rip and replace and no vendor lock-in.

Simform is a premier digital engineering services company specializing in Cloud, MACH architectures, Data, and AI to create digital experiences and scalable products. With specialized and advanced credentials in Microsoft Azure Data and AI, Simform is adept at engineering robust agentic AI systems with task-specific agents to deliver use-case-aligned outcomes. Simform provides pre-built AI accelerators and mature frameworks to speed up release cycles with tight governance models to scale agentic AI across the enterprise.

Quick facts

• Founded: 2010
• Headquarters: Orlando, Florida, USA
• Team size: 1,000–5,000 employees
• Core AI capabilities: Generative AI, data science, machine learning, MLOps, autonomous agent implementation, and agent-based AI systems.
• Key technologies: Azure AI Foundry, Azure OpenAI Service, Copilot Studio, Microsoft Fabric; Power Apps, Power Automate, Dataverse; proprietary AI accelerators — ThoughtMesh, TrueMorph, NeuVantage, Data360, CodeTools, PexAI, MedNoteDX, and ShopSavvy.
• Industry focus: Financial services, healthcare and life sciences, retail and e-commerce, supply chain and logistics, and hi-tech and digital-native companies.
• Certifications and credentials: Microsoft Solutions Partner for Data & AI Azure, Digital & App Innovation Azure, Infrastructure Azure, and Security; advanced specializations in AI Apps on Microsoft Azure, AI Platform on Microsoft Azure, and Agentic DevOps with Microsoft Azure and GitHub; Microsoft Azure Expert MSP; Microsoft Fabric Featured Partner; CMMI Level 3.
• Minimum project size: $25,000+
• Engagement models: Co-engineering teams, dedicated teams, project-based delivery, managed services.
• Notable clients: Fortune 500 and mid-market enterprises, including Red Bull, Cisco, and Fujifilm.
• Website: www.simform.com

Why the stand out

• A proprietary accelerator portfolio purpose-built for agentic AI at scale: ThoughtMesh enables governed agent deployments with secure knowledge access and corrective RAG, while accelerators like TrueMorph, NeuVantage, Data360, and CodeTools extend this across data, applications, and SDLC, supporting end-to-end AI adoption.
• Deep Microsoft Azure credentials with proven delivery at scale: Simform’s $3 million investment in Microsoft Azure reflects a longer-term direction, strengthening engineering practices, delivery models, and operating standards required to sustain agentic AI in production.
• Production-first engineering model that bridges pilots and scale: Focus on moving beyond pilots through KPI-driven systems, workflow integration, and embedded DataOps/MLOps practices such as CI/CD, drift detection, and continuous monitoring.

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology, guided by its purpose of unleashing human energy through technology for an inclusive and sustainable future. Backed by proprietary research that has shaped the industry’s understanding of agentic AI adoption, Capgemini brings both the intellectual depth and operational scale to help enterprises move from agentic AI exploration to full-scale deployment, spanning strategy, engineering, intelligent operations, and industry-specific agent solutions.

Quick facts

• Founded: 1967
• Headquarters: Paris, France (operations in more than 50 countries)
• Team size: 350,000+ employees
• Core AI capabilities: Agentic AI strategy and deployment, multi-agent orchestration, generative AI, intelligent industry automation, AI-powered business process services, sovereign AI, and responsible AI governance.
• Key technologies: NVIDIA AI Enterprise with NVIDIA NIM microservices via a dedicated agentic gallery, Microsoft Azure OpenAI and ServiceNow for enterprise workflow orchestration; Google Cloud AI; SAP AI; AWS AI/ML services
• Industry focus: Healthcare, financial services, manufacturing, and telecommunications and across automotive, consumer products, retail, life sciences, energy and utilities, and public sector.
• Certifications and recognitions: Consistently recognized by leading analyst firms including Gartner, IDC, and Forrester across AI, cloud, and intelligent operations categories; Microsoft Solutions Partner across Data & AI, Digital & App Innovation, Infrastructure, and Security
• Engagement models: Strategy and consulting, technology transformation, intelligent operations, managed services, Capgemini Invent (innovation-led transformation), Capgemini Engineering, and Global Business Services.
• Notable clients: Telenor (AI Factory for sovereign AI), and Fortune 500 enterprises across banking, automotive, retail, telecoms, and life sciences globally.
• Website: www.capgemini.com

Why they stand out

• Industry-specific agentic solutions built for production from day one: In collaboration with NVIDIA, Capgemini delivers end-to-end AI services tailored to the diverse needs of specific industries when implementing AI agents.
• A $3.3 billion strategic bet to lead agentic AI-powered intelligent operations: Capgemini’s acquisition of WNS for a total cash consideration of $3.3 billion is designed to combine capabilities and scale to address the strategic opportunity driven by agentic AI.
• Pioneering sovereign agentic AI infrastructure at national scale: Capgemini has been working with Telenor to build Norway’s first sovereign and secure AI Cloud Service in collaboration with NVIDIA.

Since 1993, EPAM Systems,  has used its software engineering expertise to become a leading global provider of digital engineering, cloud and AI-enabled transformation services, and a leading business and experience consulting partner for global enterprises and ambitious startups. What sets EPAM apart in the agentic AI space is an engineering-first philosophy. Its AI/Run™ framework, open-source DIAL orchestration platform, and purpose-built agentic toolchain are grounded in 30+ years of software delivery discipline, helping enterprises move past failed pilots and into production-grade agentic systems at scale.

Quick facts

• Founded: 1993
• Headquarters: Newtown, Pennsylvania, USA
• Team size: 50,000+ employees
• Core AI capabilities: Responsible AI, generative AI, and AI-native engineering; agentic AI strategy and deployment, multi-agent orchestration, agentic QA, AI-native SDLC transformation, open-source AI platform engineering, and AI-powered marketing.
• Key technologies: Microsoft Azure AI Foundry, Azure OpenAI, and Microsoft 365 Copilot across 17 Microsoft specializations in AI, app innovation, and data; Google Cloud Gemini Enterprise for production-ready AI agents; AI/Run™ methodology and toolchain.
• Industry focus: Financial services, consumer goods and retail, travel and hospitality, software and hi-tech, business information and media, life sciences and healthcare, insurance, industrial, and energy and resources.
• Certifications and recognitions: Microsoft Solutions Partner across Data & AI, Digital & App Innovation, Infrastructure; Named a Leader in The Forrester Wave™: Modern Application Development Services, Q1 2025
• Engagement models: Advisory and use case identification, full platform and product development, AI enablement and upskilling, Testing as a Service, managed services, and EPAM Continuum integrated consulting.
• Notable clients: PostNL, Albert Heijn Mars and Reckitt, and Fortune 500 enterprises across financial services, retail, life sciences, and telecom.
• Website:com

Why they stand out

• A comprehensive, methodology-driven framework for AI-native transformation: The AI/Run™.Transform Playbook by EPAM is a next-generation integrated consulting services framework built with AI to boost enterprise-wide AI-native transformation.In addition, EPAM DIAL, their open-source agentic orchestration platform built for enterprise control: embodies EPAM’s broader vision for balancing innovation velocity with long-term control, interoperability, and responsible governance.
• Production-proven agentic AI with documented enterprise outcomes: Real-world implementations such as multi-agent SDLC automation for PostNL and Azure-based AI platforms for Albert Heijn demonstrate production-scale outcomes, including faster development cycles and workflow efficiency gains.
• Agentic systems applied to engineering workflows: EPAM extends agentic AI into the SDLC through Agentic QA™, enabling adaptive, AI-driven testing that dynamically responds to UI changes and reduces reliance on manual and script-based automation.

At the heart of Cognizant’s agentic AI strategy is a tightly integrated suite of proprietary platforms — Neuro® AI, Agent Foundry, Flowsource™, and Skygrade™, all engineered to turn isolated AI pilots into production-grade agent networks at enterprise scale. Cognizant has built an enterprise-grade last mile for AI, complete with platforms, services, and IP that boost quality, streamline orchestration, and cut costs, so enterprises can turn AI’s raw power into lasting impact.

Quick facts

• Founded: 1994
• Headquarters: Teaneck, New Jersey, USA
• Team size: 350,000+ employees
• Core AI capabilities: Agentic AI design and deployment, multi-agent orchestration, generative AI, applied AI, intelligent automation, data engineering, AI-led operations.
• Key technologies: Microsoft Azure AI, Azure OpenAI Service, Cognizant Neuro platform, Google Agentspace, Salesforce Agentforce.
• Industry focus: BFSI, healthcare, life sciences, retail, telecommunications.
• Certifications and recognitions: Microsoft Solutions Partner across Data & AI, Digital & App Innovation, Infrastructure, and Security; Named a Leader and Star Performer in the Everest Group Artificial Intelligence and Generative AI Services PEAK Matrix® Assessment 2025.
• Engagement models: Consulting-led transformation, managed services, platform-led implementations, and enterprise AI advisory.
• Notable clients: Fortune 500 and Global 2000 enterprises across financial services, healthcare, retail, telecom, and insurance.
• Website: www.cognizant.com

Why they stand out

• A proprietary agentic AI platform ecosystem spanning the full enterprise lifecycle: Cognizant Agent Foundry gives enterprises a composable, platform-agnostic pathway to becoming agentic enterprises. Agents are built to support the full lifecycle of agent deployment across four stages: Discover, Design, Build, and Scale.
• Governed, enterprise-scale agent deployment
  With compliance support for standards like GDPR and HIPAA and large-scale internal adoption of Claude-based tooling, Cognizant enables controlled, policy-driven agent deployments across engineering and business workflows.
• Rapid multi-agent prototyping with Neuro® Accelerator
  The Neuro® AI Multi-Agent Accelerator provides no-code tooling and pre-built agent networks for functions like finance, customer service, and supply chain, allowing teams to quickly design, customize, and scale agent systems.

Globant builds agentic AI systems through its AI Studios and platform ecosystem, focusing on deploying agents within digital products, customer experiences, and enterprise workflows. Its approach combines generative AI, data platforms, and product engineering to enable agents that interact with user interfaces, business systems, and data layers. The emphasis is on embedding agents into experience-driven applications, where they can automate decisions, personalize interactions, and support real-time execution.

Quick facts

• Founded: 2003
• Headquarters: Luxembourg (operational presence globally)
• Team size: 25,000+ employees
• Core AI capabilities: Generative AI, AI engineering, data science, experience AI, intelligent automation
• Key technologies: Globant AI Studios, Microsoft Azure AI, Google Cloud AI, AWS AI/ML services, proprietary AI accelerators
• Industry focus: Media and entertainment, retail, financial services, travel and hospitality, gaming
• Certifications and credentials: Microsoft Solutions Partner across Data & AI and Digital & App Innovation; Strategic partnerships with major cloud providers for AI and data platforms
• Engagement models: Studio-based delivery, product engineering, co-creation programs, managed services
• Notable clients: Google, Electronic Arts, Disney, Santander, NHL, FIFA
• Website: www.globant.com

Why they stand out

• Agent deployment within digital products and experiences: Globant specializes in embedding agents into customer-facing applications, enabling real-time personalization, decisioning, and interaction within digital platforms.
• AI Studios for domain-specific agent use cases: Its Studio model allows teams to build agent-based solutions tailored to industries such as media, gaming, and retail, accelerating adoption through reusable patterns and expertise.
• Strong alignment with experience engineering: By combining AI with UX and product design, Globant enables agents to operate directly within user journeys, supporting tasks like recommendations, support interactions, and workflow automation.

Slalom builds agentic AI systems through a cloud-native, platform-first approach, with a strong focus on Microsoft ecosystems. Its delivery emphasizes deploying agents within enterprise applications and data environments, enabling them to retrieve context, execute tasks, and integrate with business systems. Rather than building standalone agent layers, Slalom focuses on embedding agents into cloud platforms and modern application stacks, ensuring they operate as part of production systems from the outset.

Quick facts

• Founded: 2001
• Headquarters: Seattle, Washington, USA
• Team size: 13,000+ employees
• Core AI capabilities: Generative AI, AI engineering, data and analytics, cloud-native AI, intelligent automation
• Key technologies: Microsoft Azure AI, Azure OpenAI Service, Microsoft Fabric, Databricks, Snowflake
• Industry focus: Financial services, healthcare, retail, public sector, technology
• Certifications and credentials: Microsoft Solutions Partner across Data & AI, Digital & App Innovation, and Infrastructure; founding member of Salesforce’s AI partner advisory board
• Engagement models: Consulting-led delivery, co-creation, cloud transformation programs, managed services
• Notable clients: Starbucks, Alaska Airlines, T-Mobile, Expedia Group, Microsoft
• Website: www.slalom.com

Why they stand out

• Azure-first approach to agent deployment: Slalom designs agentic systems directly on Azure platforms, enabling agents to integrate with enterprise data, applications, and services without requiring additional abstraction layers.
• Agents embedded into modern data platforms: Strong focus on connecting agents to data environments such as Fabric, Databricks, and Snowflake, allowing them to operate with real-time context and enterprise data.
• Cloud-native delivery model for faster productionization: By building within cloud-native architectures, Slalom enables faster deployment of agent-based systems that can scale with application and data workloads.

Eleks builds agentic AI systems by focusing on domain-specific applications where agents operate on structured data and defined workflows. Its approach emphasizes engineering agents for precision tasks—such as document processing, analytics, and decision support—rather than broad, open-ended use cases. By combining AI models with data engineering and custom software development, Eleks enables agents to execute reliably within enterprise systems and data environments.

Quick facts

• Founded: 1991
• Headquarters: Tallinn, Estonia
• Team size: 2,000+ employees
• Core AI capabilities: Generative AI, machine learning, data science, computer vision, NLP
• Key technologies: Microsoft Azure AI, AWS AI/ML services, Google Cloud AI, custom AI frameworks
• Industry focus: Finance, logistics, healthcare, retail, agriculture
• Certifications and credentials: Microsoft Solutions Partner for Data & AI and Digital & App Innovation; ISO-certified delivery standards (quality and information security)
• Engagement models: Custom development, dedicated teams, product engineering, consulting
• Notable clients: Aramex, Havas, Kuka
• Website: www.eleks.com

Why they stand out

• Agents designed for domain-specific execution: Eleks focuses on building agents for clearly defined tasks such as document analysis, forecasting, and operational decision support, ensuring higher accuracy and reliability.
• Strong integration with data engineering workflows: Agents are tightly coupled with data pipelines and analytics systems, enabling them to operate on structured and contextualized enterprise data.
• Engineering-led approach to custom AI systems: Eleks combines AI with custom software development, allowing agents to be embedded into applications and tailored to specific business requirements.

Ciklum builds agentic AI systems by embedding agents into API-driven architectures and microservices-based applications, enabling them to execute tasks such as data retrieval, workflow triggering, and decision routing across enterprise systems. Its delivery focuses on connecting LLM-based agents with backend services and data layers, allowing agents to operate within transactional systems like e-commerce platforms, customer service systems, and operational dashboards. The emphasis is on lightweight integration, where agents extend existing systems rather than requiring full re-architecture.

Quick facts

• Founded: 2002
• Headquarters: London, United Kingdom
• Team size: 4,000+ employees (LinkedIn)
• Core AI capabilities: Generative AI, LLM application development, data engineering, intelligent automation, AI product engineering
• Key technologies: Microsoft Azure AI, Azure OpenAI Service, AWS Bedrock, Google Vertex AI, REST/GraphQL APIs, microservices frameworks
• Industry focus: Retail (e-commerce platforms), financial services (customer operations), telecom (service automation), travel and mobility
• Certifications and credentials: Microsoft Solutions Partner for Data & AI, ISO-certified delivery standards (quality and information security)
• Engagement models: Product engineering, dedicated teams, custom AI development, managed services
• Notable clients: Metro AG, Flixbus, Just Eat Takeaway, Panasonic, P&G, Lottoland
• Website: www.ciklum.com

Why they stand out

• MCP Server Engineering making enterprise products AI-native and instantly interoperable: Ciklum’s MCP Server Engineering creates a secure access layer that connects enterprise tools, APIs, and data. This enables AI agents in platforms like Claude, Copilot, and ChatGPT to interact in real time using natural language, creating more intuitive experiences for customers and teams with no time-consuming code rewrites and no costly workarounds.
• Low-disruption deployment model: Its approach minimizes internal strain by integrating with existing systems like CRM or ERP via APIs, requiring only essential access to relevant data and workflows, resulting in rapid deployment with minimal disruption.
• Proven impact across customer service and software delivery: Ciklum applies agentic AI to customer service and software engineering workflows, enabling automation of routine queries, faster response times, and reduced manual effort. Its AI Experience Engineering approach embeds AI across the SDLC, from prototyping to post-release support.

Founded in 2015 with a clear mission to bridge critical implementation gaps in enterprise data and AI, Kanerika has rapidly built a reputation for combining deep data engineering foundations with practical agentic AI deployment. Anchored by its proprietary FLIP platform, a growing library of purpose-built AI agents, and a unique strength in Microsoft Fabric and Azure ecosystems, Kanerika builds agentic AI systems by grounding agents in enterprise data pipelines and analytics environments.

Quick facts

• Founded: 2015
• Headquarters: Princeton, New Jersey, USA
• Team size: 200–500 employees
• Core AI capabilities: Generative AI, data engineering, machine learning, analytics, intelligent automation
• Key technologies: Microsoft Azure AI, Azure OpenAI Service, Snowflake, Databricks, Azure Data Factory, ETL pipelines, data lake architectures
• Industry focus: Retail, supply chain, manufacturing, healthcare, financial services
• Certifications and credentials: Microsoft Solutions Partner for Data & AI, Databricks Consulting Partner; Everest Group’s Most Promising Data and AI Specialists.
• Engagement models: Data and AI consulting, custom development, managed services, analytics transformation
• Notable clients: Mid-market and enterprise organizations across data-intensive industries
• Website: www.kanerika.com

Why they stand out

• Data-native agent design: Kanerika builds agents directly on top of data pipelines and warehouses, enabling them to operate on structured, real-time data for tasks like reporting, forecasting, and anomaly detection.
• Strong integration with ETL and analytics workflows: Agents are embedded within data engineering processes, allowing them to trigger jobs, process data, and generate insights as part of existing workflows.
• Focus on governed, analytics-driven execution: By leveraging curated data environments, Kanerika ensures agents produce outputs that are consistent, auditable, and aligned with enterprise data models.

LeewayHertz is a leading AI consulting and development company serving tech-savvy startups, scale-ups, and enterprises — building custom AI-driven platforms and applications that redefine operational paradigms and unlock unprecedented value for clients. Intellectyx Now operating as part of The Hackett Group following its acquisition in September 2024, LeewayHertz combines deep AI engineering expertise with The Hackett Group’s strategic consulting reach. This gives it a rare end-to-end capability that spans from AI ideation all the way through implementation. At the core of its agentic AI offering is ZBrain Builder, a proprietary orchestration platform that powers intelligent agent deployment across security operations, compliance, billing, HR, IT, and industry-specific functions.

Quick facts

• Founded: 2007
• Headquarters: San Francisco, California, USA
• Team size: 200–500 employees (LinkedIn)
• Core AI capabilities: Generative AI, LLM application development, agentic AI systems, RAG pipelines, conversational AI
• Key technologies: OpenAI GPT models, Azure OpenAI Service, LangChain, vector databases (Pinecone, Weaviate), custom agent frameworks
• Industry focus: Healthcare, finance, legal, supply chain, SaaS platforms
• Certifications and credentials: Recognized by Forbes as a top AI consulting firm globally
• Engagement models: Project-based delivery, team extension model, AI consulting and strategy, ongoing support and maintenance, and proof of concept development.
• Notable clients: ESPN, NASCAR, Hershey’s, McKinsey, P&G, Siemens, 3M, and Pearson.
• Website: www.https://www.leewayhertz.com/

Why they stand out

• ZBrain Builder — a purpose-built agentic AI orchestration platform spanning the full enterprise stack: Through ZBrain Builder, LeewayHertz builds intelligent agents that enable adaptive defense mechanisms, compliance automation, and secure AI deployments, embedding contextual analysis, real-time detection, and decision-making across operational lifecycles.
• A proprietary suite of industry-specific AI products ready to deploy: LeewayHertz offers a portfolio of ready-to-use AI products including an Enterprise GenAI Platform, AI Copilot for Sales, AI Research Solution for Due Diligence, GenAI Platform for Healthcare, AI Customer Service Agent, and industry-specific GenAI platforms for Finance, Manufacturing, and Logistics.
• Comprehensive multi-agent system architecture with single and multi-agent flexibility: Leveraging advanced AI agent development tools including CrewAI and AutoGen Studio, LeewayHertz builds intelligent AI agents capable of executing a multitude of tasks including analysis, research, code generation, audits, and reviews.

Most agentic AI partner evaluations get stuck at surface-level questions like “What models do you use?” or “Can you show me a demo?” These tell you very little about whether a firm can actually deliver production-grade agentic AI in your environment. The questions below are designed to surface the gaps that only show up after you have signed the contract.

On Production Readiness

1. How many of your agentic AI deployments are in production today — not pilot, not PoC, but live in a real enterprise environment?

The agentic AI hype cycle has created an industry full of firms that can demo an impressive prototype but have never shipped an agent to production. Ask for the number, then ask for references you can call directly. A firm with 5 production deployments is worth more than one with 50 PoCs.

2. What was the longest time-to-production of any agentic AI project you have delivered, and what caused the delay?

The honest answer to this question reveals more than any success story. Delays in agentic AI projects typically stem from data access issues, hallucination rates in production, integration failures with legacy systems, or governance blockers. How a firm diagnoses and recovers from these failures tells you everything about their delivery maturity.

3. What percentage of your agentic AI projects required a significant re-architecture after the initial build, and why?

Agent systems that work in a sandbox frequently break in production when they encounter messy real-world data, unpredictable user behavior, or enterprise-scale volume. A partner who has never had to re-architect has either not shipped enough or is not being honest with you.

On Agent Reliability and Failure Modes

4. How do your agents behave when they encounter ambiguous instructions, missing context, or conflicting data?

This is the question most enterprises forget to ask. Unlike traditional software, agents make decisions and bad decisions at scale are expensive. You need to know whether the agent gracefully degrades, escalates to a human, or silently produces a wrong output. Ask for a specific example from a past engagement.

5. What is your approach to hallucination management in agentic workflows where agents are taking real-world actions and not just generating text?

Hallucinations in a chatbot are annoying. Hallucinations in an agent that is processing invoices, updating CRM records, or triggering procurement workflows are a business risk. A serious partner will have a concrete answer involving output validation layers, confidence thresholds, human-in-the-loop checkpoints, and corrective RAG strategies.

6. Can you show us your observability stack, specifically how you trace what an agent did, why it did it, and what data it acted on?

If a partner cannot show you how they monitor agent behavior in production at the action level and not just the output level, they are not ready for enterprise deployment. Regulators, auditors, and your own ops teams will eventually ask the same question.

On Integration with Existing Enterprise Systems

7. How do you handle agents that need to operate across multiple systems of record such as ERP, CRM, and HRIS that were never designed to work together?

Most enterprise environments are a patchwork of legacy systems, proprietary APIs, and inconsistent data schemas. Agentic AI that works cleanly in a greenfield environment often collapses when it hits a 15-year-old ERP. Ask the partner to walk you through a specific engagement where they solved this problem, not how they would solve it hypothetically.

8. What is your strategy when the data an agent needs is siloed, poorly labeled, or stored in unstructured formats like PDFs, emails, or scanned documents?

Agents are only as good as the data they can access and reason over. Data quality and accessibility problems are the single biggest cause of failed agentic AI deployments, yet most firms skip past this in the sales conversation. The answer should involve RAG pipelines, data cataloging, pre-processing strategies, and realistic timelines for data readiness.

9. How do you manage agent identity and permissions, and specifically how do you ensure an agent only accesses the data and systems it is authorized to use?

In a multi-agent system, each agent may be calling APIs, querying databases, and reading documents on behalf of a user. Without a rigorous identity and access management layer, you risk agents inadvertently exposing sensitive data across organizational boundaries. This question often reveals whether a partner has truly thought through enterprise security or just wrapped an LLM in a chatbot interface.

On Governance, Compliance, and Risk

10. If a deployed agent makes a decision that results in a financial loss or a compliance violation, how do you trace the root cause and who is accountable?

This is the question no one asks but everyone needs answered before go-live. Governance in agentic AI is not just about logging. It is about establishing clear accountability chains, audit trails, and rollback mechanisms. A partner that has not thought through this scenario is not ready for regulated industries.

11. How do you handle situations where an agent needs to be updated, retrained, or rolled back without disrupting live enterprise workflows that depend on it?

Agentic AI is not a one-time deployment. Models drift, business rules change, and agent behavior needs to evolve. A partner without a clear model lifecycle management and versioning strategy will leave you with a fragile system that is expensive to maintain and risky to update.

12. What is your approach to regulatory compliance in jurisdictions with strict AI governance requirements such as the EU AI Act, HIPAA, or GDPR?

This is especially critical for firms in financial services, healthcare, or operating across Europe. Ask for specifics: how do they classify AI systems under the EU AI Act’s risk tiers? How do they handle data residency requirements when agents are querying cloud-hosted LLMs? Vague answers here are a red flag.

On Commercial Structure and Long-Term Partnership

13. How do you price agentic AI engagements, and what happens to the commercial model when the agent’s scope needs to expand after go-live?

Agent scope creep is common. A system built to handle customer service inquiries often gets asked to also handle billing disputes, refund processing, and escalation routing within six months. Understand upfront whether your contract structure accommodates this evolution or whether every expansion triggers a new statement of work.

14. What does your handoff model look like — do you build and leave, or do you train our internal teams to own and evolve the agent systems you build?

Sustainable agentic AI requires internal ownership. A partner whose commercial interest depends on you remaining dependent on them for every update, retraining cycle, and integration change is not a strategic partner. They are a managed service you did not agree to. The best firms invest in knowledge transfer and internal capability building from day one.

It’s time to stop treating agentic AI as an experiment and start treating it as infrastructure. The partners you choose now will shape not just your first deployment, but your organization’s capacity to compete in a world where intelligent, autonomous systems are becoming as foundational as cloud computing was a decade ago. Choose with the same rigor you would apply to any critical infrastructure decision, because that is exactly what this is.