Context Engineering, MCP, Reasoning Models, A2A: Emerging concepts in generative AI for 2026
1. Context Engineering
Context Engineering is a methodological approach that involves designing, structuring, and optimizing all the information provided to an LLM so that it produces relevant, reliable, and business-aligned responses. It is no longer just about writing a good prompt, but about managing the entire context transmitted to the model: instructions, data, conversation history, documents, business rules, or accessible tools.
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For several years, the question was simple: how do you write the perfect prompt? In 2026, the question has changed: how do you provide the AI with the right context?
The most advanced organizations have realized that a model, no matter how powerful, cannot make sound decisions without having the right information at the right time. Corporate data, conversation history, access to business tools, memory, and governance rules: all these elements are becoming just as important as the prompt itself.
Context Engineering involves selecting and injecting the information a model needs to respond at exactly the right moment. The goal is to provide rich yet relevant context without overwhelming the model with unnecessary data. This approach significantly improves the quality, consistency, and reliability of generated responses.
2. MCP - Model Context Protocol
MCP is an open standardization protocol that allows AI models and agents to communicate uniformly with external tools, databases, applications, and services. It simplifies integrations and promotes interoperability across AI systems.
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As AI models evolve into systems capable of taking action, a new issue arises: how to enable them to interact simply with enterprise applications, databases, and tools? Until recently, each integration required specific development, which limited the scalability and interoperability of AI solutions.
The Model Context Protocol emerged to address this challenge. Often described as “the USB-C of AI,” this standard aims to unify how AI models and agents connect to their environment. Thanks to MCP, an AI can access data, execute actions, or interact with external services in a standardized way without requiring custom integration every time.
At a time when companies are deploying agents capable of searching for information, executing business processes, or orchestrating multiple tools, MCP is becoming an essential component of AI infrastructure, fostering an ecosystem that is more open, flexible, and easier to industrialize.
3. RAG
We introduced RAG—Retrieval-Augmented Generation—in our first article. In 2026, “naive” RAG has given way to a whole family of much more sophisticated multi-step architectures.
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The basic principle of RAG—retrieve then generate—remains valid, but the architectures that truly make a difference in production today rely on several additional building blocks. Hybrid Search combines vector search and keyword search (BM25) to capture both semantic similarity and lexical precision. Re-ranking uses a secondary model, often a cross-encoder, to reorder retrieved documents based on their actual relevance. Query rewriting and the HyDE (Hypothetical Document Embeddings) method reformulate the user's question to improve search quality.
Three recent developments are particularly shaping the landscape. GraphRAG connects RAG to knowledge graphs to capture relationships between entities, whereas vector search alone sees each document in a silo. Corrective RAG (CRAG) introduces a step where the model evaluates the relevance of retrieved documents and decides whether to supplement the search if necessary. Finally, Agentic RAG entrusts an agent with the decision of when and how to search, rather than systematically applying the same pipeline.
A RAG system in production in 2026 is no longer just a simple “retrieve then generate” process. It is a composite architecture that adapts its search strategy to the nature of the question asked.
4. Embeddings
In our first article, we introduced embeddings as numerical representations that capture the semantic meaning of content. In 2026, the discipline of embedding engineering has become a profession in its own right.
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The choice of embedding model and how data is prepared for representation now make a major difference in the quality of AI solutions. Embedding models have become specialized: by domain (finance, healthcare, legal), by language, and by modality. Multimodal embeddings capable of representing text, images, audio, or code within the same vector space are opening up use cases that were impossible a year ago, particularly for searching through heterogeneous document databases.
Chunking strategies—the way documents are broken down before being vectorized—have become decisive. Semantic chunking, which splits content based on meaning rather than fixed size, or hierarchical chunking, which preserves the structure of a long document, significantly improve the quality of the associated RAG.
Finally, the vector database ecosystem has stabilized around a few mature players: Pinecone, Weaviate, Qdrant, and Milvus for pure players, and pgvector for those who prefer to stay on PostgreSQL. Today, the choice is based less on raw performance and more on integration capabilities, cost at scale, and operational simplicity.
In 2026, we no longer “do embeddings.” We do representation engineering.
5. Reasoning Models
Reasoning models are AI models designed to analyze a problem step-by-step before producing a response. They prioritize reasoning and planning to improve their performance on complex tasks.
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For years, the AI race has focused on speed and the ability to generate responses in seconds. A new trend is now emerging: giving models more time to think before they respond.
Reasoning Models embody this evolution. Rather than producing an immediate response, they break complex problems down into several reasoning steps, evaluate different paths, and progressively build their solution. This capability, made possible by test-time compute, significantly improves their performance on tasks that require logic, planning, or problem-solving.
Beyond a simple improvement of LLMs, these models form one of the foundations of future agentic architectures. An agent capable of not only understanding and generating content, but also planning, deciding, and acting autonomously, requires a robust reasoning engine. Reasoning Models are that engine.
6. A2A - Agent-to-Agent
Agent-to-Agent is an open protocol that allows AI agents to communicate, collaborate, and coordinate with one another, even when they come from different platforms or providers.
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With the rise of multi-agent architectures, AI systems no longer rely on a single model capable of doing everything, but rather on several specialized agents working together. Some agents search for information, while others analyze data, make decisions, or execute actions. This evolution creates a new challenge: enabling these agents to communicate effectively despite the diversity of technologies used.
A2A was launched in 2025 to address this need. Supported by a growing ecosystem of industry players, this protocol aims to establish a common language that allows agents to exchange information, delegate tasks, and coordinate their actions.
As companies deploy systems composed of multiple specialized agents, A2A is set to become a key standard for the interoperability and orchestration of future agentic AI platforms. While MCP standardizes the relationship between an agent and its tools, A2A standardizes the relationship between agents.
7. Computer Use By Agent
Computer Use allows an AI agent to interact directly with digital interfaces just as a human user would: clicking, navigating, entering information, and using applications to complete a task.
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The first generations of AI agents relied primarily on APIs to access applications and execute actions. While this approach remains effective, it requires systems to be specifically designed for AI integration. However, many business applications, internal tools, and legacy software lack such interfaces.
Computer Use addresses this limitation by allowing agents to act directly through the user interface. Thanks to their ability to interpret what appears on the screen, they can navigate menus, fill out forms, consult documents, or trigger actions across various software programs.
This approach paves the way for automating entire processes across existing applications without requiring specific development. It serves as a key building block for future autonomous agents capable of executing end-to-end tasks in a digital environment. In banking and insurance, where the application landscape is vast and heterogeneous, this capability takes on particular importance.
8. Orchestrator
In our first article, we introduced the Orchestrator as the conductor coordinating models, agents, and tools. By 2026, orchestration has become a true software engineering discipline, complete with its own frameworks, patterns, and observability tools.
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The ecosystem of orchestration frameworks has structured itself around a few major players. LangGraph has become the standard for complex workflows with state management and conditional branching. CrewAI favors an approach based on teams of specialized agents collaborating on a common mission. AutoGen and Semantic Kernel offer other coordination models, each with its own strengths.
Orchestration patterns have emerged and stabilized. The Router pattern directs requests to the most suitable agent. Sequential and Parallel patterns chain or parallelize steps. The Hierarchical pattern establishes a hierarchy of supervisors and executors. The Swarm pattern allows multiple agents to coordinate without a single leader.
Two other dimensions have become critical. Human-in-the-loop integrates human intervention points into automated workflows, which is essential in regulated environments (banking, healthcare, defense) where certain decisions cannot be left to AI alone. Observability, powered by tools like LangSmith or Langfuse, allows for tracking workflows, measuring costs, and analyzing errors at scale. Without it, an agentic system in production quickly becomes unmanageable.
By 2026, the orchestrator is no longer an abstract concept. It is an engineering component that is selected, coded, deployed, and monitored with the same rigor as any standard business platform.
9. Vibe Coding
Vibe Coding refers to a software development approach where the user describes their intent in natural language and lets the AI generate a significant portion of the code required to achieve it.
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The emergence of AI-based development assistants has profoundly transformed the way applications are created. Where developers previously had to manually write every line of code, it is now possible to describe a feature, an interface, or a business need and let the AI propose an implementation.
Popularized in 2025, the term Vibe Coding illustrates this new way of developing, more focused on intent than on traditional programming. This approach accelerates the creation of prototypes and facilitates access to development for less technical profiles.
However, it does not replace software engineering skills, which remain essential to ensure the quality, security, and maintainability of the applications produced. Vibe Coding is an accelerator, not a substitute. It changes the distribution of roles between the developer and the machine, without removing the responsibility for design.
10. Inference Economy
The Inference Economy refers to all the challenges related to optimizing cost, performance, and resource consumption when using AI models. It aims to generate more value while reducing the cost of each request.
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For a long time, the focus was on the training costs of AI models, sometimes estimated at several million euros for the most advanced ones. With the democratization of usage and the massive deployment of AI in companies, another issue has become central: the cost of inference, i.e., the cost associated with each request processed by a model.
It is in this context that the concept of the Inference Economy has emerged. When thousands or even millions of requests are executed every day, every generated token, every call to a complex model, and every additional reasoning step has a direct impact on operational costs. Organizations are therefore seeking to optimize the balance between response quality, execution speed, and resource consumption.
Dynamic model selection based on task complexity, reducing token counts, caching reused contexts, and hybrid architectures combining local and remote models: these approaches illustrate the emergence of a new logic where economic efficiency is becoming as important as technological performance.
11. Sovereign AI
Sovereign AI refers to the ability of an organization or state to master its data, models, infrastructure, and AI operations in order to maintain control over their use and governance.
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The rapid rise of AI raises a question that has become strategic for both companies and nations: who controls the models, data, and infrastructure upon which AI systems rely?
It is against this backdrop that the concept of Sovereign AI has emerged. This approach aims to ensure local or European control over the key components of artificial intelligence: models, data, computing power, cloud platforms, and governance mechanisms. The goal is not only to protect sensitive information but also to preserve the strategic autonomy of organizations in the face of technologies that have become critical.
For highly regulated sectors—such as finance, healthcare, defense, and public services—Sovereign AI has become a critical priority. Beyond model performance, companies must consider the origin of the technologies they use, where their data is stored, and their ability to maintain control over digital assets that have become essential to their competitiveness. This approach is now vital for organizations that want to innovate without losing control of their most valuable assets.
12. Shadow AI
Shadow AI refers to the use of artificial intelligence tools by employees without official validation, supervision, or oversight from the organization.
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You’ve heard of Shadow IT? Shadow AI is its natural evolution in the world of artificial intelligence.
The rapid rise of generative AI tools has facilitated their adoption by employees, often with the goal of improving productivity or automating daily tasks. From content writing and data analysis to code generation and document summarization, many use cases are developing spontaneously within companies, sometimes without the knowledge of IT departments or governance leads.
While these initiatives can boost performance, they also raise significant concerns regarding security, compliance, and data protection. Sharing sensitive information with unapproved tools, using models with poorly understood operations, and lacking usage traceability can expose an organization to various risks.
Shadow AI has become a major issue for companies seeking to balance innovation, productivity, and AI governance. The right answer rarely lies in prohibition; it almost always involves providing an official framework that is simple and powerful enough to make workarounds unnecessary.
An ever-accelerating AI landscape
As is often the case during periods of rapid technological acceleration, vocabulary evolves almost as quickly as usage. Not all of these concepts will likely have the same long-term impact. Some are still emerging trends, while others are already finding their place in enterprise AI projects.
However, they reflect a deeper shift: AI is no longer limited to the model itself. It now extends to issues of integration (MCP, A2A), representation (advanced embeddings, context engineering), architecture (advanced RAG, orchestration), economics (inference economy), governance (sovereign AI, shadow AI), and new paradigms of action (reasoning models, computer use, vibe coding).
At Prime Analytics, we support organizations in the design, deployment, and governance of solutions that combine generative AI, advanced RAG, AI agents, MCP, and multi-agent orchestration. If you are looking to build an AI architecture that is scalable and sustainable, our experts are here to help.
To learn more about practical uses for Claude in the workplace, check out our training course Mastering Claude for Business, a one-day session designed to help your teams implement the right setups, workflows, and best practices.
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