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Capgemini reports that 2% of organizations have deployed AI agents at scale, 12% at partial scale, 23% are running pilots, and 61% are still exploring deployment. It also expects adoption to accelerate: by 2028, more than one-third of organizations will have AI agents working as members of human-supervised teams.
Most companies will start by assigning agents to specific operating tasks. In finance, that means invoice extraction, PO matching, exception flagging, and draft ERP posting. In support, it means ticket classification, CRM lookups, knowledge-base retrieval, routine case resolution, and summarized escalation. In engineering, it means codebase search, test drafting, release-note generation, and CI-linked delivery support. The next step will be multi-stage workflows such as claims handling, procurement coordination, and service resolution across several systems.
As agent use expands from narrow tasks to broader operational workflows, many organisations reach the same decision point: build the capability in-house or work with a team that already knows how to deliver it. The rest of this article looks at seven companies that work with agentic systems day to day, how they design and ship these solutions, and where each of them tends to fit best.
N-iX: multi-agent systems and enterprise environments
N-iX is a stronger fit for enterprise buyers with complex internal systems than for teams looking for a quick prototype. Its value sits in connecting agentic workflows to real infrastructure: internal data, business tools, permissions, monitoring, and controlled execution.
That matters when an AI system has to do more than answer questions. In a large organisation, an agent may need to retrieve internal knowledge, call several systems, move work across steps, and pause for human review at the right point. N-iX looks better aligned with that kind of work than with lightweight assistant projects.
For larger companies, the appeal is practical: a better fit for integration-heavy environments, long-running workflows, and production requirements.
MEV: agentic workflows for data-intensive products
MEV looks best suited to companies that want AI features shipped into software, not left at the strategy level. Its AI offering covers custom AI/ML development, generative AI, integrations, and agentic workflows, with a clear emphasis on turning those capabilities into usable product features. The stack it highlights is practical and current, including OpenAI, Anthropic, Gemini, Llama, LangChain, MCP servers, n8n, and major cloud platforms.
What stands out is MEV’s focus on workflow design and control. Its agentic orchestration model breaks work into stages, assigns roles such as extractor, planner, auditor, and executor, then adds routing, permissions, observability, testing, and production monitoring. The tooling it lists points to stateful, data-heavy systems: LangGraph, CrewAI, AutoGen, Temporal, BullMQ, REST and GraphQL integrations, Pinecone, pgvector, Postgres, and monitoring tools such as Langfuse, LangSmith, Arize Phoenix, and Sentry. That makes sense for products where an agent has to move across several systems, keep state, and remain debuggable in production.
Itransition: broad GenAI portfolio for complex systems
Itransition has a broad AI and GenAI service portfolio. Its public offering covers custom AI development, generative AI, chatbots, RAG-driven assistants, and AI agent development across healthcare, finance, insurance, telecom, manufacturing, automotive, retail, and software.
This profile fits complex environments where AI is only one layer of a larger transformation program. In Itransition’s case, assistants, retrieval, and workflow automation sit within the same delivery model. The practical implication is direct: assistants handle interaction, retrieval brings in internal context, and orchestration turns both into multi-step flows that can query systems, move data, and support business operations.
10Pearls: product-oriented generative and agentic AI
10Pearls is built around digital product delivery, with AI positioned as part of product design, engineering, and release work rather than as a separate research track. Its public offering covers generative AI development, agentic AI development, AI consulting, and product engineering, which aligns well with teams building customer-facing products and AI-enabled software features.
One of the practical strengths here is speed. 10Pearls frames its GenAI work around early assessment of data, infrastructure, and risk, then moves quickly into implementation with verification layers, fine-tuning, and controls aimed at reducing hallucinations.
That approach suits rapid POC work: test a narrow use case, validate the data path, measure output quality, and only then expand the feature across the product.
Coherent Solutions: agentic AI inside existing software ecosystems
Coherent Solutions develops AI capabilities as part of broader product engineering work. Its AI services include generative AI, conversational systems, analytics solutions, and enterprise AI integrations, with a focus on embedding these capabilities into existing software products and platforms.
Typical implementations involve conversational interfaces, AI-assisted content generation, and analytics systems connected to operational data. In these environments, AI components interact with internal services, data platforms, and business applications such as CRM systems or internal knowledge bases. The agent layer coordinates tasks such as retrieving internal information, generating responses, and triggering actions in connected systems.
Saritasa: AI agents across web, mobile, and IoT stacks
Saritasa works at the point where AI, application development, and connected systems meet. Its capabilities cover custom AI development, chatbots, IoT software, system architecture, and API integration, which is relevant when agentic systems need to operate across both software platforms and physical devices.
This becomes important in projects where agents interact with sensor data, connected hardware, or field workflows. In those environments, an agent may need to process telemetry, surface alerts, trigger actions through integrated systems, or prepare operational context for a human team. Saritasa’s work includes an AI-enabled voice assistant for RV control and monitoring, alongside broader IoT and fleet-related software delivery.
Belitsoft: LLM-centric development and agentic workflows
Belitsoft focuses heavily on LLM work at the foundation level: training, fine-tuning, prompt engineering, and domain-specific assistants. Its AI services explicitly cover custom LLM training and development for chatbots, internal assistants, and industry-specific use cases built on proprietary data.
That matters in projects where the model layer needs adaptation before the workflow layer starts to matter. In practice, this usually means assistants grounded in company data, tuned for domain terminology, and connected to internal systems through retrieval, APIs, or custom logic. Belitsoft also describes end-to-end AI agent development that includes data preparation, architecture design, implementation, testing, integration, deployment, and production support.
In an agentic setup, those pieces combine into a wider pipeline: an LLM handles interpretation and generation, retrieval brings in business context, and the agent layer coordinates actions across tools and systems. Belitsoft’s profile fits companies that need serious LLM customization without the cost profile of a large enterprise integrator.
Comparative view: mapping vendors to use cases
Outlook beyond 2026
By 2027 and 2028, the strongest systems will look less like open-ended assistants and more like bounded operators: they will have narrow tool access, explicit approval points, replayable traces, and hard limits on what they may change. Gartner’s forecast cuts both ways: it expects more than 40% of agentic AI projects to be canceled by the end of 2027, but also expects agentic AI to appear in 33% of enterprise software by 2028 and handle 15% of daily business decisions.
A second important change is standardization at the tool layer. In 2025, MCP started to matter because agent builders were tired of writing one-off integrations between models and business systems. In December 2025, Anthropic donated MCP to the Linux Foundation’s new Agentic AI Foundation, where it joined AGENTS.md and other foundational projects under neutral governance. That raises the odds that agent stacks will become more portable across models, tools, and vendors. For buyers, this matters because the next lock-in risk is no longer only the model provider. It is the proprietary runtime sitting between the model and the systems it can act on.
Architecture is also getting more opinionated. Microsoft’s guidance already treats orchestration patterns such as sequential flows, concurrent workers, handoffs, and group-chat coordination as first-class design choices, not implementation details. That is where the field is heading: fewer all-purpose agents, more graph-based systems with task decomposition, state management, and recovery logic. In practice, agent development is starting to resemble workflow engineering with LLMs inside it, not magic autonomy layered on top of an API.
Security will get much sharper. In January 2026, NIST opened an RFI focused specifically on securing AI agent systems that can take actions affecting external state. That is an important distinction. Once an agent can update records, trigger workflows, or move money, the main question is no longer whether the model answered well. The question is whether the system can constrain authority, verify actions, resist prompt injection, and keep an audit trail that survives an incident review. Beyond 2026, serious agentic systems will be judged more by permission design, identity controls, and action-level logging.
After 2026, buyers will ask tougher questions about execution, not just model output. Deloitte’s 2026 outlook highlights legacy integration as a core blocker, and that is probably the right dividing line for the vendor market. The stronger companies will be the ones that can connect agents to ERP, CRM, ticketing systems, data platforms, and approval workflows without creating cost spikes, latency problems, or control gaps. That is where vendor differentiation is heading.
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