The realm of artificial intelligence is undergoing a seismic shift. Early AI models were limited to processing mere snippets of text, but today’s cutting-edge systems possess the capacity to ingest and comprehend entire books. A significant milestone in this evolution arrived on April 5, 2025, when Meta unveiled Llama 4, a groundbreaking AI model family boasting an unprecedented 10-million-token context window. This leap forward has profound implications for the future of agentic AI systems, which are designed to operate autonomously, planning, deciding, and acting independently.
To gain deeper insights into this transformative technology, we turned to Nikita Gladkikh, a distinguished figure in the AI community. As a BrainTech Award winner, an active member of the IEEE, and a Staff Software Engineer at Primer AI, Nikita has been at the forefront of AI validation and infrastructure development. With a career spanning over a decade, starting in 2013, Nikita has seamlessly blended practical software engineering, academic research, and contributions to the global developer community, establishing himself as a sought-after expert in Python, Go, and AI-based automation. His unique perspective stems from his extensive hands-on experience in deploying large-scale LLM-powered pipelines across diverse sectors such as finance, marketplaces, and search technologies.
Nikita Gladkikh is particularly renowned for his pioneering work on scalable architectures that integrate large language models (LLMs) with robust validation logic. In this domain, reliability and accuracy are paramount, and Nikita’s strategic contributions have been instrumental in shaping the RAG-V (Retrieval-Augmented Generation with Verification) paradigm, which is rapidly gaining momentum across AI-driven industries.
The Significance of Context Window Expansion
Meta’s Llama 4 has shattered previous context window limits by expanding it to an astounding 10 million tokens, a feat achieved shortly after Google’s release of Gemini 2.5, which offered a context window of 1 million tokens. But what do these figures signify for the AI industry?
According to Nikita, the trend toward larger context windows is nothing short of transformative. By enabling AI systems to process and analyze massive volumes of input, including entire conversations, extensive documents, and even entire databases, these systems can now reason with a level of depth and continuity that was previously unattainable. This paradigm shift has a profound impact on the design of agentic pipelines, where AI agents are tasked with planning, making decisions, and executing actions independently. A larger context translates to fewer errors, enhanced personalization, and more immersive user experiences. It is a clear indicator of the direction in which the entire field is heading. The expansion of context windows fundamentally alters the capabilities of AI. Instead of dealing with short, isolated snippets of information, AI can now process entire narratives, code repositories, extensive research papers, and complex datasets. This leads to a significant improvement in the quality and relevance of AI outputs. Consider, for example, an AI tasked with summarizing a lengthy legal document. With a limited context window, the AI might struggle to grasp the overall argument and miss crucial details. However, with a 10-million-token context window, the AI can thoroughly analyze the entire document, identify key themes, and provide a comprehensive and accurate summary. Similarly, in a customer service scenario, an AI agent with a large context window can access and understand the entire history of a customer’s interactions with the company, allowing it to provide truly personalized and effective support. Furthermore, larger context windows enable AI to perform more sophisticated reasoning and inference. They can identify subtle relationships and patterns in data that would be impossible to detect with limited context. This is particularly valuable in fields such as scientific research, where AI can be used to analyze vast amounts of data and uncover new insights. The ability to process and understand large amounts of information is also crucial for building more robust and reliable AI systems. By exposing AI to a wider range of data, we can reduce the risk of bias and ensure that the AI is capable of handling a variety of real-world scenarios. Future progress in context window technology is projected to continue. This expansion will involve not only larger token counts but also improved mechanisms for managing and processing contextual information efficiently. The development of techniques like hierarchical context management and attention mechanisms will be crucial for handling the complexities of extremely large context windows.
Hands-on Experience and Agentic Pipeline Design
Nikita’s extensive experience in building developer tools like PKonfig and educational platforms used at scale provides valuable insights into the intricacies of agentic pipeline design. He emphasizes the critical importance of modularity, observability, and failure isolation when building systems that must operate reliably under pressure.
Drawing from his experience, Nikita advocates treating every component as a potential point of failure and implementing fallback paths, validation layers, and reproducibility measures. These principles are directly applicable to the design of agentic workflows, where agents require structured state management, traceable execution, and deterministic behavior, just like any distributed system. The design of effective agentic pipelines requires a deep understanding of the challenges and opportunities presented by autonomous AI systems. Modularity is essential for creating scalable and maintainable pipelines. By breaking down complex tasks into smaller, independent modules, developers facilitate easier debugging, testing, and modification. Observability is crucial for monitoring the performance of agentic pipelines and identifying potential issues. This involves collecting and analyzing data on various metrics, such as latency, error rates, and resource utilization. Failure isolation is vital for preventing failures in one part of the pipeline from affecting other parts. This can be achieved through techniques such as circuit breakers and retry mechanisms. Furthermore, reproducibility is paramount for ensuring the consistency and reliability of agentic pipelines. This requires careful attention to detail, including version control, dependency management, and environment configuration. In addition to these technical considerations, the design of agentic pipelines must also take into account the ethical implications of autonomous AI systems. It is important to ensure that agents are not biased or discriminatory, and that their actions are aligned with human values. This requires careful attention to the training data used to develop the agents, as well as ongoing monitoring and evaluation of their performance. Nikita’s work on PKonfig highlights the importance of configuration management in agentic pipelines. PKonfig provides a centralized and consistent way to manage configuration parameters, making it easier to deploy and manage complex AI systems. His experience with educational platforms underscores the need for user-friendly interfaces and intuitive workflows. By making it easy for users to interact with agentic pipelines, we can increase their adoption and impact. Agentic pipelines are particularly useful in automating complex tasks that require human-level intelligence. Examples include customer service, content creation, and financial analysis. By automating these tasks, we can free up humans to focus on more creative and strategic work.
Enhancing AI Reliability through Expanded Context
The advancements in context window size are already making a tangible impact on production systems, enhancing AI reliability in various applications. Nikita provides a concrete example of how larger contexts improve AI reliability:
Smaller context windows often forced AI models to truncate crucial contextual information, leading to fragmented or inaccurate outputs. However, with context windows expanding to millions of tokens, models can now retain extensive historical interactions, detailed user profiles, and multi-dimensional relationships within data. For instance, an AI-based customer support agent can reference past interactions spanning years, providing contextually rich, highly personalized support. This significantly reduces errors caused by context loss, thereby enhancing the reliability and depth of AI-driven decisions, especially in critical scenarios such as healthcare diagnostics or financial forecasting. The increased context provided by larger context windows enables AI models to make more informed and accurate decisions. In healthcare, for example, an AI diagnostic tool with access to a patient’s complete medical history can provide more reliable diagnoses than a tool that only has access to a limited subset of information. Similarly, in finance, an AI-powered trading system with access to historical market data and real-time news feeds can make more profitable trading decisions. The ability to retain extensive historical interactions is particularly valuable in customer service applications. An AI customer support agent with access to a customer’s past interactions can provide more personalized and effective support by understanding the customer’s needs and preferences. This can lead to increased customer satisfaction and loyalty. Furthermore, larger context windows enable AI models to better understand complex relationships and dependencies in data. This is important in a variety of applications, such as fraud detection, where AI models need to identify subtle patterns of fraudulent behavior. The expansion of context windows also has implications for the explainability of AI models. By providing more context, we can make it easier to understand why an AI model made a particular decision. This is particularly important in high-stakes applications, where it is crucial to be able to explain the reasoning behind an AI model’s decisions. The use of larger context windows is not without its challenges. Processing and managing large amounts of contextual information can be computationally expensive. However, advancements in hardware and software are making it increasingly feasible to work with large context windows. Another challenge is ensuring that AI models do not overfit to the training data and that they are able to generalize to new situations. This requires careful attention to the design of the AI model and the selection of training data.
Nikita recalls a challenge faced while implementing Retrieval-Augmented Generation with Verification (RAG-V) at Primer AI: reducing the data for validation calls to fit supporting documents into the context. This limitation restricted the precision of their validation efforts. However, with Llama 4’s expanded context window, those barriers are effectively removed.
RAG-V: The Cornerstone of Trusted AI Development
The RAG-V method, where models retrieve and verify content, has emerged as a cornerstone of trusted AI development. Nikita explains that RAG-V is a method where the AI doesn’t just generate answers, but actively verifies them against trusted external sources – in essence, real-time fact-checking.
Nikita’s work on RAG-V emphasizes the integration of validation principles within agentic AI systems. RAG-V employs retrieval systems and robust verification layers to cross-reference model outputs against authoritative external sources. For example, in financial risk assessments, each piece of generated advice or prediction is validated against historical market data or regulatory compliance documents. Expanded context windows enhance this approach by enabling richer contexts and emphasizing the need to validate content and format. RAG-V is particularly well-suited for applications where accuracy and reliability are paramount. By verifying AI model outputs against trusted external sources, we can reduce the risk of errors and ensure that the AI is providing accurate and reliable information. The use of retrieval systems allows AI models to access a vast amount of knowledge, enabling them to provide more comprehensive and informative answers. The verification layers in RAG-V help to ensure that the information retrieved is accurate and relevant. The expansion of context windows enhances the effectiveness of RAG-V by allowing AI models to consider more contextual information when retrieving and verifying content. This leads to more accurate and reliable results. In financial risk assessments, RAG-V can be used to validate AI-generated predictions against historical market data and regulatory compliance documents. This helps to ensure that the predictions are accurate and that the AI is complying with all relevant regulations. RAG-V can also be used to improve the explainability of AI models. By providing access to the external sources used to verify the AI model’s outputs, we can make it easier to understand why the AI model made a particular decision. This is particularly important in high-stakes applications, where it is crucial to be able to explain the reasoning behind an AI model’s decisions. The implementation of RAG-V requires careful attention to the design of the retrieval systems and the verification layers. The retrieval systems must be efficient and accurate, and the verification layers must be robust and reliable. The choice of external sources is also crucial. The sources must be trustworthy and authoritative. Furthermore, the integration of RAG-V into existing AI systems requires careful planning and execution. The integration must be seamless and efficient, and it must not introduce any new risks or vulnerabilities.
Nikita emphasizes that larger context windows amplify the benefits of RAG-V by allowing more supporting material to be included in a single validation cycle. However, they also increase the risk of unstructured output. He cautions that language models should not be treated as deterministic Web API invocations but rather as probabilistic entities, akin to intelligent users. Therefore, both content and structural validation are essential to ensure reliability and integration readiness.
LLMs as User Inputs: A Paradigm Shift in Software Architecture
Nikita suggests that treating LLM outputs more like user inputs than API responses has a profound impact on modern software architecture. When LLMs are viewed as user-like inputs, rather than static API calls, it fundamentally alters the way software is designed and built.
Frontend interfaces must be designed to handle uncertainty and delay gracefully, employing patterns like optimistic UI. On the backend, asynchronous, event-driven designs become essential, with message queues (e.g., Kafka or RabbitMQ) helping decouple AI-driven actions from core logic. The shift in perspective from treating LLMs as APIs to treating them as user inputs necessitates a fundamental rethinking of software architecture. Traditionally, APIs are designed to be deterministic and predictable, with well-defined input and output formats. However, LLMs are inherently probabilistic and generate outputs that can vary depending on the input, the model’s internal state, and other factors. This uncertainty requires a more flexible and resilient software architecture. Frontend interfaces must be designed to handle the variability in LLM outputs gracefully. Optimistic UI patterns can be used to provide a smooth user experience, even when the LLM is taking time to generate a response. On the backend, asynchronous and event-driven architectures are essential for decoupling AI-driven actions from core logic. Message queues such as Kafka or RabbitMQ can be used to manage the flow of messages between different components of the system. This allows the system to continue functioning even when the LLM is experiencing delays or failures. The use of hybrid architectures, which combine traditional code with model-based decisions, provides a fallback mechanism when LLM outputs are slow or unreliable. This ensures that the system can continue to function even in the absence of LLM support. The variability of LLM outputs underscores the critical importance of validation, not just for accuracy but also for structure and consistency. Tools like PKonfig, developed by Nikita, enforce schema-compliant responses, ensuring integration reliability in probabilistic systems. This helps to ensure that the LLM outputs are consistent and that they can be easily integrated into existing systems. The paradigm shift towards treating LLMs as user inputs has significant implications for software development practices. It requires a greater emphasis on testing, monitoring, and debugging. It also requires a more collaborative approach to software development, with close collaboration between developers, data scientists, and domain experts.
Hybrid architectures, which combine traditional code with model-based decisions, allow for fallback mechanisms when LLM outputs are slow or unreliable. This variability underscores the critical importance of validation, not just for accuracy but also for structure and consistency. Tools like PKonfig, developed by Nikita, enforce schema-compliant responses, ensuring integration reliability in probabilistic systems.
Transforming Education with LLMs: Automated Grading and Personalized Feedback
Nikita has applied these principles not only in industry but also in education, developing an automated grading platform for GoIT. He explains that his experience has reinforced the value of determinism, reproducibility, and human-in-the-loop escalation. Even as we integrate more advanced tools like LLMs, these concepts remain central.
Modern LLMs have the potential to revolutionize student feedback by offering more personalized and context-aware responses. Instead of relying on fixed templates, an LLM could adapt its explanations to a student’s learning history, coding style, or native language, making feedback more accessible and actionable. However, Nikita stresses that reliability and fairness remain non-negotiable. This necessitates combining LLMs with retrieval-based grounding, rubric validation, and override mechanisms. Just as explainability and auditability guided the design of the original platform, Nikita envisions the future of AI-assisted education as agentic, but with strict safeguards and transparent logic at every step. The application of LLMs in education has the potential to transform the learning experience for students. Automated grading platforms can free up teachers to focus on more personalized instruction. Personalized feedback can help students to learn more effectively by providing them with targeted guidance. Context-aware responses can ensure that the feedback is relevant to the student’s individual needs and learning style. However, it is important to ensure that the use of LLMs in education is fair and equitable. The LLMs must be trained on data that is representative of all students, and the feedback they provide must be unbiased and accurate. It is also important to ensure that teachers have the ability to override the LLM’s feedback when necessary.
Retrieval-based grounding can help to improve the accuracy and reliability of LLM feedback. Rubric validation can ensure that the feedback is aligned with the learning objectives. Override mechanisms can allow teachers to correct any errors or biases in the LLM’s feedback.
Strategies for Managing Complexity in AI Development
Addressing the architectural and validation challenges inherent in AI development requires effective strategies for managing complexity. Nikita advises developers to prioritize validation from the outset, embedding schema checks throughout the pipeline. He emphasizes the importance of using tools that enforce structure and consistency, not just correctness. The architectural and validation challenges inherent in AI development necessitate effective strategies for managing complexity. Prioritizing validation from the outset, embedding schema checks throughout the pipeline, is crucial for ensuring the reliability and accuracy of AI systems. The utilization of tools that enforce structure and consistency, not just correctness, is paramount for building robust and maintainable AI systems. The modularity of AI systems is essential for managing complexity. By breaking down complex tasks into smaller, independent modules, developers can facilitate easier debugging, testing, and modification. The observability of AI systems is crucial for monitoring their performance and identifying potential issues. This involves collecting and analyzing data on various metrics, such as latency, error rates, and resource utilization. The use of fallback mechanisms is important for ensuring that AI systems can continue to function even when they encounter errors or failures. These mechanisms can include techniques such as circuit breakers and retry mechanisms. Testing is critical for identifying and correcting errors in AI systems. This includes unit testing, integration testing, and end-to-end testing. The use of version control systems is essential for managing changes to AI systems. This allows developers to track changes, revert to previous versions, and collaborate effectively. The documentation of AI systems is crucial for ensuring that they can be easily understood and maintained. This includes documenting the architecture, the code, and the data. The use of automated tools can help to automate many of the tasks involved in AI development, such as testing, validation, and deployment.
Drawing from his experiences and recognizing the need to think modularly, Nikita advocates for separating model logic from business logic and building robust fallbacks for cases where the model is incorrect or slow. This combination of technical discipline and strategic foresight is crucial for building reliable AI systems.
The Influence of Recognition and Community Involvement
Nikita’s recognition through initiatives like the BrainTech Award and his involvement with communities like IEEE have significantly influenced his approach to tackling complexities in practice. These experiences have instilled in him the importance of bridging innovation with practicality.
The BrainTech Award recognized Nikita’s work on applying computer vision to streamline real-world user workflows, which emphasized not only technical capability but also usability at scale. This experience shaped his belief that AI systems must be both powerful and seamlessly integrated into existing processes. His ongoing involvement with IEEE keeps him grounded in the latest research and best practices, enabling him to design systems that are not only advanced but also ethical, modular, and resilient in production. The recognition that Nikita has received through initiatives like the BrainTech Award demonstrates the value of his work and the impact that it has had on the field of AI. This recognition has helped to raise his profile and to attract more opportunities to collaborate with other researchers and practitioners. His involvement with communities like IEEE has provided him with access to a wealth of knowledge and expertise. This has helped him to stay up-to-date on the latest research and best practices, and to develop innovative solutions to complex problems. Nikita’s approach to tackling complexities in practice is influenced by his belief that AI systems must be both powerful and seamlessly integrated into existing processes. He believes that AI should be used to augment human capabilities, not to replace them. He also believes that AI systems should be designed to be ethical, modular, and resilient in production.
Shaping the Future of AI
Nikita’s future work will focus on building robust, scalable, and ethically sound AI systems. He believes that models like Llama 4 and Gemini 2.5, with their massive context windows, have transformative potential, especially in education. These models could enable AI tutors to provide personalized, context-rich explanations based on a student’s full learning history. The future of AI hinges on the development of robust, scalable, and ethically sound systems. These systems must be capable of handling complex tasks, adapting to changing environments, and operating in a manner that is consistent with human values. Models like Llama 4 and Gemini 2.5, with their massive context windows, represent a significant step forward in this direction. These models have the potential to enable AI tutors to provide personalized, context-rich explanations based on a student’s full learning history. They also have the potential to automate many of the tasks involved in education, such as grading and assessment. However, it is important to ensure that these models are used in a responsible and ethical manner. They must be trained on data that is representative of all students, and the feedback they provide must be unbiased and accurate. It is also important to ensure that teachers have the ability to override the models’ feedback when necessary. The development of robust, scalable, and ethically sound AI systems requires a collaborative effort involving researchers, practitioners, policymakers, and the public. It is important to engage in open and transparent discussions about the potential benefits and risks of AI, and to develop regulations and guidelines that ensure that AI is used in a way that benefits society as a whole.
Automated assessment is another key area of focus. Nikita’s grading tool for GoIT already handles syntax and correctness at scale. However, next-generation LLMs have the potential to push this further by assessing conceptual understanding, tailoring feedback to prior performance, and aligning results with academic standards via RAG-V.
To ensure reliability, Nikita emphasizes the continued need for schema validation and fallback logic, principles that underpin tools like PKonfig. By combining advanced models with structured validation, we can enhance education without compromising trust, fairness, or pedagogical rigor.
Balancing Scalability with Educational Rigor
Supporting thousands of students each quarter requires a careful balance between scalability and pedagogical integrity. Nikita achieved this by separating concerns: automation handled routine validations, such as test results and code formatting, while complex edge cases were flagged for human review. This ensured high throughput without compromising feedback quality or fairness. The challenge of balancing scalability with pedagogical rigor in the context of supporting thousands of students each quarter requires a carefully considered approach. Separating concerns, with automation handling routine validations such as test results and code formatting, while complex edge cases are flagged for human review, is a key strategy for achieving this balance. This approach ensures high throughput without compromising feedback quality or fairness. The implementation of structured rubrics, version control for assignments, and traceable grading logic are also crucial for maintaining educational rigor. These measures build student trust and instructional transparency. The use of large language models (LLMs) such as Llama 4 has the potential to significantly shift this balance by enabling context-aware, multilingual, and even code-specific feedback generation at scale. These models can help to explain abstract concepts in simpler terms, tailor feedback to individual learners, and simulate tutor-like interactions. However, it is important to recognize that scale does not eliminate the need for guardrails. LLMs must be grounded in rubrics, validated against known outputs, and auditable by instructors.
Educational rigor was maintained by enforcing structured rubrics, version control for assignments, and traceable grading logic. These measures built student trust and instructional transparency.
Nikita believes that Llama 4-level models could significantly shift this balance by enabling context-aware, multilingual, and even code-specific feedback generation at scale. They can help explain abstract concepts in simpler terms, tailor feedback to individual learners, and simulate tutor-like interactions. However, he cautions that scale doesn’t eliminate the need for guardrails. LLMs must be grounded in rubrics, validated against known outputs, and auditable by instructors. With the right architecture, combining deterministic pipelines with LLM-powered personalization, we could dramatically increase access to quality education without sacrificing academic standards.
Nikita summarizes his vision as: “I build systems that don’t just work — they teach, validate, configure, and support decision-making.”