Artificial intelligence is transforming. Earlier models could only understand a paragraph, but today’s leading systems can process entire books. On April 5, 2025, Meta introduced Llama 4 to the AI community, an AI model family featuring an extraordinary 10-million-token context window. To understand what this leap means for the future of agentic AI systems, we spoke with Nikita Gladkikh, BrainTech Award winner, IEEE member, and Staff Software Engineer at Primer AI, who has led critical work on AI validation and infrastructure. Since 2013, Nikita has combined practical software engineering, academic research, and contributions to the global developer community, becoming a recognized expert in Python, Go, and AI-based automation. As one of the few specialists with hands-on experience deploying large-scale LLM-powered pipelines across finance, marketplaces, and search technologies, Nikita brings a rare, practice-driven perspective on these emerging technologies.
Nikita Gladkikh is best known for pioneering scalable architectures that combine large language models (LLMs) with rigorous validation logic—a field where reliability and correctness are as vital as innovation. His strategic input has played a pivotal role in evolving the RAG-V (Retrieval-Augmented Generation with Verification) paradigm, which is now gaining traction across AI-focused industries.
Meta’s Llama 4 has dramatically expanded the context window size to 10 million tokens, following shortly after Google released Gemini 2.5, which offered a context window of 1 million tokens. What do these numbers mean for the industry?
This trend toward larger context windows is transformative. By handling massive volumes of input — entire conversations, documents, and even databases — AI systems can now reason with depth and continuity that was previously impossible. This fundamentally changes how we design agentic pipelines, systems where AI agents plan, decide, and act independently. Larger context means fewer mistakes, better personalization, and richer user experiences. It’s a clear signal of where the entire field is heading.
Speaking of applied AI systems, you’ve built developer tools like PKonfig and educational platforms used at scale. How does that hands-on systems design experience inform your view on agentic pipelines today?
Hands-on experience building production-grade tools like PKonfig and large-scale educational platforms has made me acutely aware of the importance of modularity, observability, and failure isolation – essential for agentic pipelines. When designing systems that must operate reliably under load, I’ve learned to treat every component as a potential point of failure and to create with fallback paths, validation layers, and reproducibility in mind. These principles directly inform the design of agentic workflows: agents need structured state management, traceable execution, and deterministic behavior, just like any distributed system.
My work in applied AI, especially reducing hallucinations in resume summarization and automating feedback in educational settings, reinforces the importance of verification loops and retrieval-first design. Agents cannot be trusted unquestioningly; they need embedded validation and tight integration with structured knowledge bases. Human-in-the-loop design is also essential, something I prioritized in educational tools and now view as critical for ensuring agent accountability. Ultimately, agentic pipelines are not just novel UX flows; they’re software systems, and treating them with the rigor of backend engineering makes them viable in practice.
From theory to practice, these advancements are already shaping the production systems. Can you give a concrete example of how these larger contexts improve AI reliability?
Certainly, previously, smaller context windows forced AI models to truncate crucial contextual information, resulting in fragmented or inaccurate outputs. With context windows expanding to millions of tokens, models can retain extensive historical interactions, detailed user profiles, and multi-dimensional relationships within data. For example, an AI-based customer support agent can reference past interactions spanning years, providing contextually rich, highly personalized support. This drastically minimizes errors due to context loss, greatly enhancing the reliability and depth of AI-driven decisions, particularly in critical scenarios such as healthcare diagnostics or financial forecasting. While implementing Retrieval-Augmented Generation with Verification (RAG-V) in Primer AI, we struggled to reduce the data for validation calls to fit supporting documents into the context. That limited the precision of our validation. Llama 4 removes those barriers.
The RAG-V method, where models retrieve and verify content, has become a cornerstone of trusted AI development. Can you explain RAG-V and how expanded context windows improve its validation capabilities? How does your work on RAG-V at Primer AI directly enhance validation practices with expanded context windows?
RAG-V – Retrieval-Augmented Generation with Verification – is a method where the AI doesn’t just generate answers, but actively verifies them against trusted external sources. Think of it as fact-checking in real time.
My work on RAG-V deeply integrates the validation philosophy within agentic AI systems. RAG-V uses retrieval systems and robust verification layers to cross-reference model outputs against authoritative external sources. For instance, 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. Larger context windows amplify this by allowing more supporting material to be included in a single validation cycle. However, they also increase the risk of unstructured output. That’s why we say: language models are not APIs – they’re more like intelligent users. Both content and structural validation are essential to ensure reliability and integration readiness.
LLM calls should not be treated as deterministic Web API invocations. LLMs are probabilistic and should be treated more like user inputs than deterministic APIs.
You’ve suggested treating LLM outputs more like user inputs than API responses. What impact does this have on modern software architecture?
Treating LLMs as user-like inputs, rather than static API calls, fundamentally changes software architecture. Frontend interfaces must handle uncertainty and delay gracefully, using patterns like optimistic UI. Asynchronous, event-driven designs are essential on the backend, with message queues (e.g., Kafka or RabbitMQ) helping decouple AI-driven actions from core logic.
Combining traditional code with model-based decisions, hybrid architectures allow fallback mechanisms when LLM outputs are slow or unreliable.
This variability makes validation critical: not just for accuracy, but for structure and consistency. Tools like PKonfig, which I developed, enforce schema-compliant responses, helping ensure integration reliability in probabilistic systems.
You’ve applied these principles not only in industry but also in education. Could you speak about your automated grading platform for GoIT and how modern LLMs could transform student feedback?
At GoIT, I designed and implemented an automated grading platform that handled thousands of student Python submissions, providing instant, consistent feedback based on a structured rubric. The system combined test-driven validation, rule-based code analysis, and metadata extraction to assess correctness and code quality, reducing grading time from days to seconds. This experience reinforced the value of determinism, reproducibility, and human-in-the-loop escalation, which remain central even as we integrate more advanced tools like LLMs.
Modern LLMs open the door to far more personalized and context-aware feedback. Instead of 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, reliability and fairness remain non-negotiable, which means combining LLMs with retrieval-based grounding, rubric validation, and override mechanisms. Just as explainability and auditability guided the original platform design, I see the future of AI-assisted education as agentic, but with strict safeguards and transparent logic at every step.
Considering these architectural and validation challenges, what are effective strategies for developers to manage these complexities?
Developers should prioritize validation from the start, embedding schema checks throughout the pipeline. Embed schema checks everywhere. Use tools that enforce structure and consistency, not just correctness. My work, recognized through initiatives like the BrainTech Award and informed by communities like IEEE, consistently highlights the need to think modularly: separate your model logic from your business logic, and build robust fallbacks for when the model is wrong or slow. This blend of technical discipline and strategic foresight is crucial.
Building reliable AI systems requires both technical discipline and strategic foresight. Given your BrainTech Award and your IEEE involvement, how have these experiences influenced your approach to tackling such complexities in practice?
These experiences have taught me to bridge innovation with practicality. The BrainTech Award recognized my work on applying computer vision to streamline real-world user workflows, an effort that emphasized not just technical capability but usability at scale. That early experience shaped my belief that AI systems must be powerful and seamlessly integrated into existing processes. My ongoing involvement with IEEE keeps me grounded in the latest research and best practices, helping me design systems that are not only advanced but also ethical, modular, and resilient in production.
Looking forward, how will your ongoing advancements shape the future of AI?
My future work will focus on building robust, scalable, and ethically sound AI systems. Models like Llama 4 and Gemini 2.5 — with their massive context windows — have transformative potential for education. They enable AI tutors to provide personalized, context-rich explanations based on a student’s full learning history.
One key area is automated assessment. My grading tool for GoIT already handles syntax and correctness at scale. Still, next-gen LLMs can push this further: assessing conceptual understanding, tailoring feedback to prior performance, and aligning results with academic standards via RAG-V.
To ensure reliability, we must still enforce schema validation and fallback logic — principles behind tools like PKonfig. Combining advanced models with structured validation can enhance education without compromising trust, fairness, or pedagogical rigor.
Since your platform supports thousands of students quarterly, how do you balance AI scalability with educational rigor? And could Llama 4-level models change the landscape of EdTech at that scale?
Supporting thousands of students each quarter required designing for both scale and pedagogical integrity. I achieved this by separating concerns: automation handled routine validations, like test results and code formatting, while complex edge cases were flagged for human review. This ensured high throughput without compromising feedback quality or fairness. Educational rigor came from enforcing structured rubrics, version control for assignments, and traceable grading logic, all of which helped build student trust and instructional transparency.
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. But 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.
As Nikita summarizes:
“I build systems that don’t just work — they teach, validate, configure, and support decision-making.”
