This year OpenAI celebrated their ten year anniversary and it’s been 3 years, 1 month, and 3 days since the release of ChatGPT). As with new technology, it’s been a gold rush. AI startups are raising unprecedented amounts of Venture Capital, data-center builds are popping up all over the world, and WallStreet oscillates between bullish and bearish with fears of a bubble, but it’s also obvious that AI will have real world implications outside of generating massive amounts of slop or allowing creeps to put girls in bikinis on X. Artificial intelligence is the foundational technology enabling these advancements, powering systems that can make autonomous decisions and drive complex, goal-oriented behavior in real-world applications.

An agentic AI system is an advanced form of artificial intelligence composed of autonomous, goal-driven agents capable of performing specific subtasks. These systems exhibit autonomy, adaptability, and are designed to operate with a higher degree of independence than traditional AI, allowing them to coordinate and achieve complex objectives with minimal human oversight.

Looking to the future, by 2026, agentic AI systems are expected to be integrated across various industries as "autonomous workers," fundamentally transforming workflows and shaping the evolution of work and strategic planning.

Enterprise AI Integration: The Beginning was Just Text

For the first year of LLMs text prediction was the obvious application, I could stare into a blank chat interface and theoretically have a conversation with the most intelligent thing in the world. LLMs helped with writing, content ideation, and gave feedback for $20/mo where consultants could cost thousands.

Enterprises could see the potential, but the training set limited an LLMs efficacy to only public information. You could spend millions training a custom model, fine tuning on data, but feedback loops were long and the integration of new data is cost prohibitive. In the context of enterprise AI, organizations recognized the strategic role of AI in driving digital transformation, automating processes, and enhancing competitiveness. Artificial intelligence is now a core driver of automation, decision-making, and digital transformation in large organizations. AI technology and comprehensive AI solutions support a wide range of business functions—including HR, IT, finance, and supply chain management—by enabling automation, predictive analytics, and process optimization. However, successful enterprise AI integration requires careful planning, starting with defining organizational goals and broader objectives as the first step. Building a cross-functional team and investing in robust AI infrastructure, efficient systems that can scale to meet the needs of large organizations, and skilled personnel are also essential for effective implementation. Enterprise AI can automate routine tasks, mundane tasks, and repetitive tasks, optimizing business processes to boost productivity and free employees for more strategic work. Automating repetitive and mundane tasks with AI tools provides a competitive advantage and helps organizations achieve their broader objectives. Enterprise AI also facilitates more informed, data-driven decision-making and enables organizations to extract valuable insights across various business operations.

By 2024, patterns emerged. RAG became the talk of every applied AI engineer. Build a complex search architecture, embed unstructured data, and allow a large language model (LLM) to retrieve the data and pack its context with new information. All of a sudden, decades of enterprise knowledge could become a part of the LLMs brain. RAG felt like looking through a foggy window, you could see what LLMs were capable of, but the picture wasn’t clear. Retrieval was brittle. Embeddings missed nuance. Chunking strategies felt like guesswork. And the user experience was still fundamentally passive: a user would submit user queries, and the LLM would answer. Handling these user queries effectively requires providing relevant information and the right context to the model to ensure accurate and useful responses. RAG proved that LLMs could work with private data but search and retrieval alone wasn’t enough. Enterprises didn’t just need LLMs that could read, they needed LLMs that could do. Data science plays a critical role in building and optimizing these AI models, and achieving high quality data through robust data collection is a crucial step for ensuring data quality and effective model performance. Data management, data scientists, and AI experts are essential for maintaining data quality, governance, and effective AI integration within enterprise environments.

After data collection, managing customer data and sensitive data is critical, requiring robust security measures to prevent data breaches and ensure compliance with regulations. Implementing user access controls is also vital to safeguard sensitive data and customer data, ensuring only authorized users can access confidential information.

This is where context engineering became critical. Designing the full context, carefully selecting and optimizing the entire set of information, instructions, and relevant data provided to the LLM, plays a crucial role in guiding its performance, especially for agentic AI and advanced workflows. Context engineering optimizes the utility of tokens against the constraints of large language models to achieve desired outcomes. Effective context engineering involves curating and maintaining the optimal set of tokens during LLM inference, ensuring the model receives the most relevant information. The goal is to find the smallest possible set of high-signal tokens that maximize the likelihood of a desired outcome. Integrating AI with existing business systems and existing systems can be complex and time-consuming, making seamless integration essential to minimize disruption and ensure operational efficiency. Adoption of enterprise AI can be challenging due to the time and effort required to learn new systems, employee hesitancy to change, and resistance ch aracterized by the 'fear of becoming obsolete.' Successful AI integration also requires addressing challenges such as high implementation costs, data security risks, and workforce impacts. Ethical and responsible use of AI is paramount, with regulatory concerns such as algorithmic bias and data privacy breaches requiring careful governance to ensure trustworthiness and compliance.

AI algorithms, machine learning, and natural language processing are foundational to enterprise AI applications, powering automation, predictive analytics, and improved customer interactions. Generative AI plays a transformative role in content creation, automation, and personalization within enterprise workflows. In software development, AI enables code generation, debugging, and accelerates the development cycle, while in healthcare, AI is poised to shrink the world's health gap by advancing drug discovery, diagnosis, and treatment planning. AI also analyzes customer behavior and provides personalized support to enhance customer experiences, and is used in supply chain and supply chain management to optimize logistics and predict demand. Integrating new systems and designing for minimal human intervention are key to operational efficiency, though organizations often face the same story of recurring challenges in enterprise AI adoption. Ultimately, AI enables organizations to solve complex problems and make more informed decisions.

Looking ahead, AI factories will emerge as organizations create infrastructure to speed up AI model development, and by 2026, enterprises will prioritize unstructured data as a primary source for AI innovation. AI is increasingly used for tracking carbon emissions and optimizing energy use to meet sustainability goals. Quantum computing is also beginning to play a role in advancing AI research and hybrid computing systems. Leading AI products like IBM watsonx™ and Microsoft Azure AI offer a range of services, including machine learning and cognitive services, supporting the evolving needs of enterprise AI integration.

Let’s give ai agents tools

By giving LLM applications the ability to execute code, call APIs, and read/write systems, we’ve started to wipe the fog off the window. This is where agents come in. Agents can reason about a goal, break tasks into steps, use tools to accomplish those steps, perform actions, and perform tasks, including specific tasks such as analyzing data from various sources. Agentic AI agents can autonomously interact with external systems through APIs and databases, enabling them to execute complex operations without human intervention. An ai system composed of multiple custom AI agents can maintain long-term goals, manage multistep problem-solving tasks, and automate complex processes and workflows across industries.

Enterprises come with thousands of services, CRMs, ERPs, ticketing systems, data warehouses, and legacy APIs. In 2025, the question was “can we build agents?” In 2026, the question is “how do we integrate agents into everything we already have?” Agentic AI can be applied to manage logistics and production end-to-end, as well as analyze data from diverse sources to optimize supply chains and predict demand. And so, in 2026 The agent will not be the product. The integration will be.

Patterns are new in context engineering

There’s no clear best way to build agentic systems yet. We’re in the “Rails 1.0” phase, patterns are forming, but nothing is canonical. Abstractions like LangChain, LangGraph, and SpringAI are helping teams move faster, but they’re evolving weekly. What worked in Q1 will be an anti-pattern by Q3.

Effective context engineering has emerged as a discipline unto itself. It’s not enough to stuff a prompt with instructions, agent engineers need to think carefully about what the model knows, when it knows it, and how that context degrades over long-running tasks. This is an iterative process, requiring repeated cycles of refining context and instructions to optimize agent performance. Techniques like agentic memory, where agents use structured note-taking to write notes outside the main context window, allow them to maintain critical context and dependencies across complex tasks. To maintain state over extended workflows, sub-agent architectures can be used, enabling specialized agents to handle focused tasks while a main agent coordinates their efforts. When building AI agents, designing the system prompt to include important context and additional context is essential for ensuring the agent’s outputs are accurate and reliable.

AI orchestration is also key, as it coordinates the efforts of multiple agents, automates workflows, tracks progress, and manages resource usage. Agentic AI is evolving towards multi-agent orchestration, where specialized agents collaborate to manage business operations end-to-end.

MCP is proof, we just don’t know what we’re doing yet. Even as Anthropic attempts to standardize how agents interact with external tools and data sources every answer creates ten more question, who owns the integration? Is it the platform team? The AI team? A new “agent infrastructure” team? How is security handled? Where does AuthN/Z come into play? What does integration o11y look like? There are no paved roads for Agents and their integrations.

We have experience

This isn’t new work. It’s familiar work in a new context. For decades, software teams have built abstraction layers over legacy systems. API or domain teams wrapped mainframes for web apps, created massive graphs for mobile and built APIs so partners could integrate.

Agentic AI should leverage these same patterns. Instead of abstracting for a mobile app, we’re abstracting for an agent. The agent becomes a consumer of your services, just like any other client. Agentic AI can autonomously perform and optimize business processes, complex processes, and complex workflows, enabling organizations to automate intricate tasks that typically require human oversight. The difference is that the agent is non-deterministic (but so are people), which means abstractions need to be more robust, error messages need to be more descriptive, and contracts need to be clearer. Agentic AI has the potential to revolutionize various industries by automating complex processes and optimizing workflows, managing entire workflows that were once controlled by humans. For example, in financial services, agentic AI can help automate fraud detection and risk assessment. Enterprises with strong API design skills, good observability practices, and a culture of clear documentation will have a massive advantage.

New surface for attacks requires robust security measures

Chat interfaces are imperfect, and I would bet they are where the real LLM alpha is. Chats open new attack vectors from prompt injection to jailbreaks. And as agents gain more capabilities (executing code, writing to databases, sending emails), the impact of a successful attack grows. Implementing agentic AI requires careful consideration and evaluation of business needs and resources to ensure the right fit and readiness for such advanced systems, especially when building AI chatbots.

Test Driven Development was a niche practice, for Agentic applications we can leverage this pattern starting with evals and reduce risk. Agent Engineers need to test what happens when users try to manipulate the agent. Transparency issues in agentic AI, often referred to as the "black box" problem, arise from a lack of clarity in decision pathways, making it crucial to address and improve transparency in these systems.

Today teams shipping agents don’t have robust eval suites. They’re moving fast, which is fine, but 2026 is the year that changes. The companies that treat evals as a first-class concern will build trust, and be able to add complexity to their Agents where others struggle. Robust governance is also essential for agentic AI, including established guidelines, regulations, and oversight mechanisms to ensure responsible deployment and ongoing management.

The Integration Imperative for compound ai systems

2025 was about proving agents could work. 2026 is about integration. The hard problems aren’t model capabilities, models are a commodity. In 2026 we will need to solve organizational problems. We need to solve: Who owns the agent platform? How do you govern tool access? How do you test systems that are non-deterministic by design? How do you secure a surface area that didn’t exist two years ago? Leveraging advanced AI capabilities, such as NLP, computer vision, and pattern recognition. will be key to driving integration and operational efficiency.

The enterprises that win won’t be the ones with the most sophisticated models or an enterprise GPT. It will be the ones that figure out integration, they will connect agents to the messy, sprawling, legacy-laden reality of enterprise software. Enterprises that adopt agentic systems early will gain a structural and operational advantage over competitors and as agentic AI scales, strong AI governance will become essential for organizations hoping to move beyond pilot projects. Additionally, AI literacy and automation skills will be critical for employee growth and advancement in 2026.

‍