In this issue, the momentum around Agentic AI deserves an exclusive edition.
We spotlight how Akvelon transforms advanced AI architecture into measurable business outcomes.
Our new Agentic MCP Platform coordinates multiple specialized agents that work on specific tasks, achieving what single-agent assistants cannot.
In a recent client project, this platform reduced analysis time from four days to just one hour.
For a deeper perspective, our new Agentic AI Whitepaper provides actionable guidance on cost optimization and secure enterprise adoption.
The Context Problem in Enterprise AI
Enterprise systems are rarely simple. To understand a project, you also have to work across external systems like Jira, GitHub, and CI/CD tools on top of large, interconnected codebases, long histories, and interdependent workflows where each step depends on the last.
Traditional, single-model assistants struggle to handle this complexity. Context fragments across stages, intent drifts over time, and parallel tasks lose alignment, leading to rework, inconsistent outcomes, and slower delivery.
Key takeaway: This is not a bigger-prompt problem; it is an architecture problem.
Achieving reliability at enterprise scale requires agents that can share context, coordinate decisions, and deliver one coherent, verifiable result.
That’s precisely what Akvelon’s Agentic MCP Platform was designed to do.
Akvelon’s Agentic MCP Platform: Turning Complex Work Into One Trusted Result
Akvelon’s Agentic MCP Platform orchestrates purpose-built sub-agents that share persistent context and operate within structured, auditable workflows.
Instead of relying on a single model to complete an entire process, the platform coordinates multiple specialized agents — each responsible for a distinct step — and merges their outputs into one verified result.
Main capabilities of Akvelon’s Agentic MCP Platform:
- Compose single-purpose sub-agents for multi-step tasks.
- Manage long context without cross-talk or intent drift.
- Build composable workflows and reusable blocks.
- Operate as an MCP consumer — connect to third-party MCP servers (e.g., Azure DevOps, GitHub, MS Learn) to use their tools/data within workflows.
- Operate as an MCP producer — run our own MCP server to expose workflows/tools to any MCP-capable client (IDE/chat).
What it solves:
- Brownfield complexity: structure large, aging repos, and long histories.
- Inconsistent outputs: produce one verified artifact (docs, reports, reviews) from repeatable workflows.
- Toolchain friction: stay vendor-agnostic and plug into existing dev tools and processes via MCP.
The impact of this approach is reflected in our recent client success story below.
Client Success: From 4 Days to 1 Hour With Agentic MCP
A global technology company faced a massive challenge: a 30 GB legacy repository with more than 100,000 artifacts and little documentation. Manual review and analysis of this system took nearly four full workdays per collection of artifacts, which slowed onboarding and made refactoring difficult. Read the full story here on Akvelon’s website.
Using Akvelon’s Agentic MCP Platform, our team built a custom orchestration that divided the review process into specialized, single-goal steps. Each step was executed in parallel by sub-agents operating under a shared context, and their results were merged into one coherent, verified artifact.
This architecture eliminated context loss and intent drift while keeping decisions consistent across the workflow.
Measured Impact
- Time savings: 4 workdays → 1 hour per representative collection of artifacts.
- Deliverable: structured, searchable documentation enabling faster onboarding and targeted refactoring.
- Qualitative impact: higher clarity across the system, fewer repeated manual reviews, and improved engineering confidence.
Akvelon’s Agentic MCP: Enterprise Use Cases
Akvelon’s Agentic MCP Platform provides a foundation for scalable automation in various enterprise environments. Its architecture can be adjusted to accelerate engineering workflows, documentation, testing, and compliance efforts in complex systems.
Selected Use Cases
- Codebase comprehension and refactoring: rapidly structure and document large legacy repositories for modernization.
- Automated quality and security review: apply rule-based sub-agents to detect inconsistencies, outdated dependencies, and policy violations.
- Test case generation and validation: automatically create and verify tests aligned with evolving business logic.
- Knowledge extraction and documentation: convert fragmented code comments, commits, and tickets into unified and searchable knowledge assets.
- Process governance: enable auditable, repeatable workflows that align with enterprise compliance requirements.
Whether applied to brownfield systems, enterprise data pipelines, or large-scale integrations, the platform delivers reliability, transparency, and measurable acceleration across the software lifecycle.
To explore how MCP-based orchestration could streamline your workflows, contact Akvelon — let’s map the potential together.
Testing Agents on Business Logic: What Worked
In our ongoing From Apps to Agents series on LinkedIn, inspired by Satya Nadella’s “agent-first” vision, we’ve been testing what it takes for agents to replace traditional apps.
First, we explored API-first agents – safe and observable, but too rigid when endpoints were missing. Then, we tried the SQL-first approach with Microsoft’s MCP Server — flexible, but risky without context or guardrails. Both approaches proved that structure and semantics matter more than access itself.
Now, we’ve taken the next step, giving an agent direct access to Akvelon’s HRM database to test it on real tasks, such as lookups, record updates, and project creation.
Instead of relying on a backend or API layer, the agent worked directly with the SQL layer and business logic. The results were revealing:
- With raw SQL access, it handled simple CRUD tasks, but failed on multi-table operations requiring business logic.
- Adding technical documentation (table relations, constraints) improved accuracy.
- Layering natural-language instructions and light human corrections brought the best results, and opened the path to persistent “learning” through reusable rules.
The main takeaway is that even strong LLMs require context, structure, and guardrails to perform reliably in production environments.
Our next step would be to make the data environment itself more informative, so agents can reason from the data, not just the prompt. Stay tuned!
New Whitepaper: Secure and Enterprise-Ready Agentic AI for Cost Reduction
Hospitals, banks, and shippers are already realizing the impact of AI agents: Providence cut patient-message load by 30%, BNY runs custom agents for 14,000 employees, and FourKites automated 80% of routine operations while cutting integration costs by 75%.
Download the Agentic AI for Cost Reduction whitepaper to learn how to achieve measurable savings with governed, agentic AI.
In the whitepaper, you’ll find:
- Rapid-ROI use cases across finance, healthcare, manufacturing, logistics, and insurance
- A security & governance checklist for compliant, controlled rollouts
- Implementation patterns and KPIs for measurable savings
- 11 real-world examples with outcomes you can benchmark
Use our guide and our expertise to turn agent potential into measurable savings.
Join Us at These Upcoming Events!
Kate Nyzhehorodtseva, Akvelon’s Director of New Business Development, will be attending:
- Web Summit 2025 · November 10-13, 2025 · Lisbon, Portugal
- Slush 2025 · November 19-20, 2025 · Lisbon, Portugal
To connect with Kate at these events or to set up a virtual meeting, reach out via LinkedIn messages.
Insights from AI Con USA in Seattle
At AI Con USA in Seattle, Kate Nyzhehorodtseva noticed how the focus has shifted: it’s no longer about what AI can do, but how to scale it responsibly.
Here’s what that means for businesses today:
- As AI moves into production, companies are transitioning solutions from pilots and labs to focus on making them reliable, observable, and efficient.
- Governance is no longer a side note. Privacy, bias, and accountability now decide whether enterprises can confidently expand AI.
- Success depends on people: the speed at which teams learn, adapt, and build trust in AI is what determines real results.
At Akvelon, we’re seeing the same shift with our clients — a growing focus on automating workflows, strengthening MLOps, and scaling AI safely so innovation leads to real results.
Keep up with the latest tech news, carefully curated and analyzed by Akvelon's experts:
- OpenAI × NVIDIA: A $100B Leap in AI Infrastructure: OpenAI and NVIDIA have announced a historic 10 GW infrastructure partnership featuring millions of next-gen Vera Rubin GPUs. For enterprises, this means greater computing capacity, lower latency, and access to more capable models. At Akvelon, we help teams translate this new power into measurable results, making AI workloads portable, cost-efficient, and ready to scale as the first 2026 gigawatt comes online.
- Microsoft Invests $30B to Build the UK’s Largest Supercomputer: Microsoft’s latest commitment marks another milestone in global AI scale – 23,000 GPUs powering the UK’s largest supercomputer.
As AI becomes the backbone of business infrastructure, Akvelon enables clients to automate complex workflows and scale MLOps, ensuring they’re ready to grow with the opportunities ahead.
Share your feedback and ideas about what you’d like to see on Akvelon’s LinkedIn by emailing info@akvelon.com!