Manos Koumentakis

Associate Manager Apps Dev @ Accenture

Speaker's Bio

Associate Manager with over 8 years of experience in full-stack web development, specializing in building scalable and high-performance digital platforms.
Experienced in designing modern web solutions and leveraging cloud technologies to support reliable and efficient software delivery.
Focus on mentoring engineers, supporting team development, and promoting strong engineering practices.
Experience working across diverse industries, contributing to projects that balance technical quality with business needs. Maintains an active interest in emerging technologies and evolving approaches to software development.

Associate Manager with over 8 years of experience in full-stack web development, specializing in building scalable and high-performance digital platforms.
Experienced in designing modern web solutions and leveraging cloud technologies to support reliable and efficient software delivery.
Focus on mentoring engineers, supporting team development, and promoting strong engineering practices.
Experience working across diverse industries, contributing to projects that balance technical quality with business needs. Maintains an active interest in emerging technologies and evolving approaches to software development.

2026 AI Cultural Impact Data Analysis Environmental Impact Space
AI-Native Engineering

Earth, Reimagined: Unlocking Hidden Insights with AI and Copernicus

TBA (CET)
TBA

What if satellite data could both safeguard our water and uncover traces of lost history? This session explores innovative solutions powered by Copernicus Earth observation data, transforming raw satellite signals into meaningful insights for water quality monitoring and archaeological discovery. By bridging environmental intelligence with cultural heritage exploration, it reveals how space-based data can illuminate what’s hidden, protect what’s vital, and enable smarter, more sustainable decisions here on Earth.

If logs were storytellers, what could we learn from their stories?

What is logging, in one word? Cost. Undeniably.
Development cost. Performance cost. Operational cost. Maintenance cost.

But is logging truly costly, or merely expensive?
As with education, the answer depends on outcomes, and those outcomes only become visible over time.

By approaching logging the way we approach learning, we can transform a controversial expense into an operational necessity.
We employ a discreet but continuous source of insight across business, development, architecture and design, security, and incident response.

When designed intentionally, logs tell stories about both system and human behaviors.
They reveal how our services actually run and where assumptions quietly fail.
In that sense, observability through logging is not a sunk cost, but a high-return investment in understanding and decision-making, potentially offering a multi-aspect business intelligence solution.

In this talk, we explore how to extract real value from logging by mapping it to three universal dimensions of learning:
-Things we know that we know
-Things we know that we do not know
-Things we do not know that we do not know

Agentile Teams: Where AI, Platform Engineering, and Human Creativity Redefine Software Delivery

What if your engineering team could move at the speed of AI – without sacrificing quality, security, or control? Enter the Agentile Team: a lean, AI-powered, and platform-enabled evolution of Agile, designed to amplify human creativity.

In this session, we’ll explore how Agentile teams codify architectural tradeoffs (security, capacity, cost) into specs, templates, and guardrails, enabling rapid experimentation and smarter decision-making from day one. Drawing on real-world experiments and platform engineering principles, Suzanne will share practical strategies for developers and platform engineers to thrive in this new era of agentic, high-velocity software delivery.

Context Engineering Workshop: Make the most out of your AI Tools

Most developers use AI coding tools like a search engine: type a question, hope for a good answer. But the teams getting real results are engineering the context: configuring system prompts, structuring project knowledge, and managing the AI’s working memory like a first-class architectural concern.

This workshop is built around a real coding project. Participants will receive a small application to build – and throughout the session, they’ll apply each technique as they learn it, experiencing firsthand how context engineering transforms their AI’s output quality.

We’ll cover:

– **The context window** – what tokens are, what counts as context, and why your AI gets worse mid-conversation (context rot)
– **Basic prompting techniques** – prompt engineering is no longer as important, but can still improve your results
– **System prompts as project configuration** – set up instruction files (CLAUDE.md, copilot-instructions.md) that make every AI interaction project-aware
– **Grounding techniques** – anchor AI output in real data instead of letting it guess, using file uploads, documentation references, and structured retrieval

Each concept is immediately put into practice on the project – so by the end, participants will have both a working application and a repeatable playbook for getting better results from any AI coding tool.

Targeted to software engineers, bring a laptop with an AI coding assistant installed.

The Missing Protocol: How MCP Bridges LLMs and Data Streams

Nobody’s talking about this: MCP isn’t just another way to build chatbots. It’s the bridge we’ve been missing between AI reasoning and real-time data systems.
Teams build AI applications that work great in demos but fall apart with production data. Your agents analyze historical reports but can’t tell what’s happening in your Kafka streams. They’re blind to schema changes and disconnected from events that matter to your business.
Instead of treating streaming platforms like black boxes, you expose them directly to your agents via MCP protocol. Suddenly, your AI doesn’t just read about data—it lives inside your data flows.
Learn what becomes possible when you stop thinking about AI as an external service and start treating it as part of your streaming architecture. We’ll build systems where agents subscribe to real-time events, reason about evolving schemas, and make decisions that ripple through your data platform.

Architectural Katas: Practicing System Design

Architectural Katas were created by Ted Neward to solve a problem: architects rarely practice their craft. Most people design systems only a handful of times in their careers, so they never develop the judgment that comes from repetition.

The kata format creates those practice opportunities. Teams work on realistic architectural problems, make design decisions, and defend those decisions when challenged. The learning happens when you see other teams solve the same problem differently and understand the assumptions behind each approach.

We run these sessions at Epignosis because our engineers need that practice too. It’s one of the few ways to develop architectural judgment outside of production systems.

This workshop is for anyone who wants that practice. You might be a developer learning to think architecturally, a tech lead seeking feedback on your decisions, or an architect wanting more repetitions. The format works because it forces clear articulation and exposes you to alternatives.

Elephant Carpaccio: the art of thin slicing

Most features arrive late, break things, and hide complexity until it hurts. Branch deployments are a rigged game: merge early and you break things, merge late and integration becomes a nightmare. Whatever you do, you lose. The only way to win is to stop playing.

Enter Elephant Carpaccio, a software development exercise that teaches vertical slicing, incremental delivery, and how to keep software deployable at all times. Instead of delivering the whole elephant at once, you learn to carve it into the thinnest possible slices: each one small, end-to-end, deployable, and providing visible progress. The goal isn’t to finish the feature. It’s to learn how to evolve software safely.

In this hands-on workshop, you’ll practice exactly that. You’ll feel the difference between fat slices and thin ones, and walk away with a technique you can apply the very next day.

If you’ve got PTSD from releases, this one’s for you.

Disclaimer: No actual elephants will be harmed during this workshop