Aleksandr Filippov
Aleksandr Filippov is a Lead Product Manager, AI Platforms & Agents, based in Limassol, Cyprus. He writes the production code behind the AI platforms he leads: retrieval systems that ground answers in knowledge you can trust, the agent runtimes and orchestration around them, Model Context Protocol servers and gateways that put both in front of any AI client, and the Python services underneath. The product side — problem selection, quality criteria, stakeholders, adoption — sits on top of work he can build himself.
Away from work he trains at the gym on a programme he runs himself, high intensity and low volume, and spends the rest of his time with his family. He keeps a close eye on where AI is actually moving, tries each shift against whatever he is building at the time, and has a few ideas of his own in progress that are not ready to show yet.
This site gathers his projects, his writing, and the thinking behind them.
How I Build
I turn frontier AI capabilities into reliable products and reusable platforms.
Product ownership
I start with the problem: users, constraints, success criteria, and the adoption path — before any code.
Platform thinking
I build reusable capabilities — retrieval, gateways, agent runtimes — rather than isolated AI demos.
AI-native engineering
Coding agents multiply my implementation speed; architecture, review, and accountability for outcomes stay mine.
Evaluation and verification
I define invariants, regression tests, and quality gates before trusting model or system behavior.
Production and adoption
A system counts when it is deployed, observed, supported, and measurably used.
Posts by Author
Agent Experience: Preparing Your Site for Its Second Reader
Every page on your site is two documents: the one a browser renders for people and the one a parser reads. AI agents read the second one. Here is what I learned making this site legible to them -- and what a machine reader finds on the personal sites of eight of the best-known people in AI, from a hidden Order of the Unicorn to a robots.txt that redirects into itself forever.
Dynamic Workflows: When the Agent Writes Its Own Harness
For a few weeks now, Claude Code has been writing its own multi-agent harness for each task -- and it quietly retired the orchestration code I used to maintain by hand. Here is what changed, the patterns worth knowing, and the research idea I think sits underneath it.
Field Notes: Building a Chest X-ray Draft Auditor with Two Tiny 4B Models — MedGemma and NVIDIA Nemotron
A Field Notes write-up on building a chest X-ray draft auditor from open data alone: two tiny 4B models, a deterministic comparator, and the evaluation-integrity lesson that nearly cost me the better model.
MCP Context Server: Persistent Memory for Your AI Coding Agents
Your AI coding agent loses its plan every time the context window compacts. MCP Context Server fixes that with persistent, searchable memory -- one Docker command and your agents remember everything.
Simplifying BPMN: A Beginner's Guide to Process Modeling in Software Development
This guide breaks down the BPMN standard, highlighting key elements and their application in software development projects, with practical examples and resource recommendations.
Navigating Success: Universal Metrics for Product Managers, with Insights from Fictional AGInnovate
Discover indispensable metrics every product manager should master, illustrated with AGInnovate’s story, for informed decision-making and fostering growth across all sectors.