01
Strategy & roadmap
I identify opportunities with you, set priorities and connect AI initiatives to your business objectives.
Clearer priorities and a practical direction for adoption.
MIKHAIL MALYSHEV / TECHNOLOGY & AI EXECUTIVE
Enterprise AI,
from strategy
to execution.
I help leaders and teams turn AI into a business capability — from strategy and skills to secure, practical delivery.
I combine strategy and organisational change with hands-on experience in AI agents, RAG and on-premises LLMs, working with organisations from startups to large enterprise groups.
20+ YEARS IN ENTERPRISE IT
Strategy grounded in hands-on experience.
EX-MICROSOFT / EX-ADOBE01 / SERVICES
Build AI capability
with purpose.
I work with leaders and teams at different stages of AI adoption, connecting strategic decisions with practical action.
02
Adoption & capability
I develop AI skills with leaders and teams, shape ways of working and build internal capability.
A team better equipped for sustainable AI adoption.
03
Technology & delivery
Drawing on hands-on experience with AI agents, RAG and on-premises LLMs, I work with your team to shape architecture and guide delivery.
A technical direction shaped around your constraints.
04
Governance & security
I bring security, governance and responsible AI considerations into adoption and delivery decisions.
A clearer view of security and governance across your use cases.
I adapt the scope and outputs to your organisation, priorities and starting point.
Discuss your AI priorities ↗02 / APPROACH
From direction
to lasting capability.
I connect strategy, people and delivery around your goals, with security and governance informing decisions throughout.
- 01
Strategy
I clarify goals, identify opportunities and help set priorities.
- 02
Enablement
I develop practical skills and support AI adoption across teams.
- 03
Delivery
I translate priorities into solutions and guide practical implementation.
- 04
Governance
I bring security, responsibility and oversight into the work.
03 / FOR DEVELOPERS
Explore the tools.
Build with intent.
I maintain a public directory of AI agent frameworks and related tools. Start with a few distinct approaches, then explore the wider landscape.
MY OPEN-SOURCE DIRECTORY · GITHUB
Awesome AI Agents Frameworks
Browse my evolving directory of agent frameworks, orchestration, coding tools, model runtimes and related projects.
Frameworks to explore
I organise the landscape by the work you are doing — from a first experiment to an enterprise AI ecosystem.
01 / START ↗
Explore the basics
Try a model, an agent interface and an initial workflow.
OPEN WEBUI · N8N · OLLAMAView starting points ↗ 02 / BUILD ↗Agents & knowledge
Build agents, process documents and retrieve relevant information.
LANGGRAPH · PYDANTIC AI · HAYSTACKExplore frameworks ↗ 03 / INTEGRATE ↗Connect & protect
Connect models, systems and tools with explicit access controls.
MCP · KEYCLOAK · LITELLMExplore integration ↗ 04 / OPERATE ↗Evaluate & govern
Test, observe and manage AI systems as adoption grows.
MLFLOW · OPENTELEMETRY · OPAExplore operations ↗These stages are a learning path, not a prescription. Tools overlap and serve different purposes; choose only what your use case needs.
Explore my enterprise AI ecosystem guide ↗04 / PRACTICAL INSIGHTS
Start with
better questions.
I use these starting points to turn AI ideas into practical, testable work — before choosing a framework or committing to an architecture.
Define the agent's job
Choose one task, define the expected output and decide which actions require a person to review or approve them.
Can you describe a successful result and a safe failure?
Choose the smallest useful stack
Compare tools against your needs: model access, data sources, orchestration, deployment and the skills your team already has.
Does every added component solve a specific problem?
Evaluate before expanding
Keep a representative set of tasks, inspect results and traces, and review permissions as the workflow changes.
Can your team detect and investigate a failure?
RELATED READINGMLflow guide to tracing and agent observability
05 / LEARNING
Learn by
doing.
I have selected these starting points from my learning collection for readers who want to understand AI and build practical skills.
FOUNDATIONS · MICROSOFT
Generative AI for Beginners
Lessons and examples for getting started with generative AI applications.
Open course ↗ AGENTS · HUGGING FACEAI Agents Course
Explore agent concepts, tools and practical examples.
Open course ↗ EXAMPLES · OPENAIOpenAI Cookbook
Code examples and guides for developers building with the OpenAI API.
Explore examples ↗ AI ENGINEERING · FULL STACKFull Stack Deep Learning
Explore the lifecycle of building and deploying AI products.
Explore resources ↗ ML FOUNDATIONS · GOOGLEMachine Learning Crash Course
Learn core machine learning concepts through lessons, visualisations and exercises.
Open course ↗ DEEP LEARNING · FAST.AIPractical Deep Learning for Coders
Apply deep learning to practical problems through a course designed for people with coding experience.
Open course ↗ LLM ENGINEERING · GITHUBBuild an LLM from Scratch
Explore a step-by-step implementation of a language model in PyTorch.
Explore repository ↗These resources are maintained by their respective creators. Check each course for its prerequisites and current access options.
THE FUTURES LIBRARY / 30 BOOKS
Yesterday’s fiction.
Today’s questions.
I explore how the imagined worlds of science fiction connect with the technologies and choices shaping our lives today.
Explore the libraryLET'S CONNECT
What could AI
make possible for you?
I would welcome a conversation about your AI strategy, adoption, delivery or governance priorities.
Or connect on LinkedIn ↗