In 2025, Akvelon focused on the challenges engineering and technology leaders face today: scaling AI beyond pilots safely and predictably, managing cloud costs and complexity, integrating automation into brownfield environments, and delivering measurable business value.
The list below compiles the articles that best represent Akvelon’s engineering work in 2025. We selected them for their practical relevance, depth of technical insight, and usefulness for teams building and operating complex enterprise systems.
Across these articles, topics span AI automation in healthcare workflows, agent-ready enterprise architectures designed to scale under real-world constraints, MLOps infrastructure with dynamic GPU scheduling on GKE, and DevOps for large-scale data systems. We also included a deeper, long-form Agentic AI whitepaper for teams looking for concrete rollout patterns, governance guidance, and real-world examples across industries.
1. Akvelon’s Agentic MCP Platform: Scalable Automation for Brownfield Products
Most enterprise platforms aren’t built to be automated. This article examines how teams can introduce automation into existing systems without destabilizing them.
Instead of treating modernization as a rewrite, our approach focuses on incremental change. Using the Agentic MCP platform, teams add AI-driven workflows alongside existing functionality, extending system capabilities while keeping core components intact. The article also highlights common failure points, such as tight coupling, hidden dependencies, and unclear ownership, and how to avoid them.
For engineering leaders responsible for long-running products, this piece outlines a realistic path to automation when greenfield architecture isn’t available.
2. From Apps to AI Agents: Reimagining Enterprise Systems Through Agentic Architecture
What does it take to turn enterprise systems into agent-ready workflow, and not break governance and reliability?
This article explores how agentic architectures differ from monolithic systems and what it takes to introduce AI agents without breaking existing platforms. It frames the shift as an evolution of enterprise architecture, from application-centric systems to agentic architecture thinking.
As AI capabilities evolve, many organizations discover that app-centric architectures struggle to support autonomous workflows without becoming brittle or unpredictable.
The article walks through practical design choices, such as isolating agents through clear contracts, using orchestration layers to control execution, and keeping state and decision logic explicit — approaches that allow teams to add autonomy while maintaining reliability and control.
For CTOs and architecture leaders working with legacy systems and regulatory constraints, this piece demonstrates how to scale AI-powered workflows in a controlled, maintainable way without letting system complexity spiral out of control.
3. From Ideas to Impact: How Akvelon Uses AI to Accelerate Client Projects
Many organizations experiment with AI but struggle to move it toward production systems with measurable results. This article details how teams can move AI into real delivery workflows, drawing on Akvelon’s project experience. Instead of treating AI as a separate initiative, our article explains how AI can be embedded directly into engineering processes. We outline how teams reduced setup and orchestration time from days to seconds and accelerated project delivery by up to 60%.
It also explains what changes when you treat AI as part of the delivery infrastructure, and how you can maintain predictable release cycles at scale.
4. AI Agents for Cost Efficiency, Speed, and Compliance
How do you roll out AI agents at scale while reducing costs and meeting security and compliance requirements?
This Agentic AI for Cost Reduction whitepaper focuses on the practical side of agentic AI in healthcare, finance and banking, manufacturing, logistics, cybersecurity, insurance, and customer operations. Readers will learn how to design, deploy, and govern AI agents to gain efficiencies, reduce costs, speed up core processes, and remain secure and compliant in real enterprise environments.
The material is grounded in real-world evidence, with practical examples across the industries, and a clear ROI framework that covers efficiency gains, risk reduction, and operational resilience. It also includes rollout playbooks and governance checklists that teams can apply directly.
This whitepaper is most useful for executives evaluating AI investments, as well as IT, security, and engineering leaders responsible for deploying AI in production environments.
Download the whitepaper to explore where agentic AI delivers measurable value in practice.
In addition to the general Agentic AI whitepaper, we’ve published shorter, industry-focused editions for teams working in areas where we see the highest adoption and demand:
- AI Agents in Healthcare — focused on operational efficiency, clinical workflows, and compliance.
- AI Agents in Logistics & Supply Chain — focused on planning, coordination, and predictive operations.
- AI Agents in Finance & Banking — focused on process automation, cost optimization, and governance.
These versions are tailored to industry-specific constraints and use cases.
5. How to Run Flyte on GKE Autopilot With Dynamic GPU Scheduling
How do we run GPU-heavy ML workloads in Kubernetes without paying for idle GPUs?
In this article, Akvelon’s Lead DevOps Engineer, Vlado Djerek walks through how to run Flyte on GKE Autopilot with dynamic GPU scheduling, based on real infrastructure constraints. The focus is on allocating GPU resources only when they’re needed, rather than keeping them reserved and idle. The piece looks closely at practical setup and trade-offs — cost control, scheduling behavior, and keeping ML workflows responsive as demand changes. For platform and ML infrastructure teams, it’s a strong signal of what MLOps practicality looks like in production.
6. Smarter Port Operations: Predicting Vessel Discharge Time With Machine Learning
In port operations, minor delays quickly turn into significant losses. This article demonstrates how machine learning can predict vessel discharge time in real terminal conditions.
Instead of relying on static estimates, the team works with real terminal data — crane availability, gang assignments, and yard congestion — and sees just how inconsistent and noisy it is. Our piece explains how these signals are combined into a forecasting model that reflects how ports actually operate.
More reliable forecasts help planners prepare berths earlier, align staffing with actual workload, and reduce downstream delays. For logistics and supply chain teams, this article shows how machine learning supports everyday operational decisions, not just reporting or post-fact analysis.
What Stood Out in 2025, and What Comes Next
Across these articles, one theme is consistent: AI delivers value when it’s integrated into real systems, cloud cost decisions are made early, and modernization succeeds through incremental and pragmatic change.
If you’re working on legacy modernization, AI in production, or cloud and MLOps efficiency, use this roundup as a map. Start with the article that matches your main constraint — governance, GPU cost, operational predictability, or brownfield integration. If you need deeper guidance on deploying AI agents with measurable ROI and compliance in mind, the Agentic AI whitepaper is a strong next step.
