The AI coding assistant economy operates on a fundamental misalignment:
Models are rewarded for appearing productive rather than being correct, users lack time to verify outputs, and economic incentives favor speed over quality.
This article examines how training incentives, verification costs, and market dynamics create patterns that often lead to low-quality code. Based on direct observation of model behavior patterns in conversations.
๐๐๐ฆ๐ ๐๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป ๐ฝ๐๐ฏ๐น๐ถ๐๐ต๐ฒ๐ ๐๐๐ โ ๐ฎ๐ป ๐ข๐ฅ๐ ๐ณ๐ผ๐ฟ ๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ถ๐ป ๐๐๐ ๐ฆ๐๐๐๐ฒ๐บ๐ Just as Hibernate abstracts databases for transactions, CEF abstracts knowledge stores for Context Engineering. Build, test, and benchmark intelligent context models in minutes, without the complexity of enterprise graph infrastructure.
Deterministic AI Design with Capability OS: Save from the AI Bubble - Live demo of Omni Agent
Everyone is piloting agents, copilots and AI platforms. Very few are asking a harder question: which of these systems will still be trusted when the AI bubble bursts? In this session I'll share my 1.5-year journey from raw LLM experiments and messy AI-generated code to a deterministic, decision-first architecture for agentic systems. I will demo Omni Agentโ-โa Capability OS for Enterprise AIโ-โand then walk through how it is designed and built using Decision-Driven Software Engineering (DDSE) and the Agentic Contract Model (ACM) so that execution stays bounded, auditable and aligned to your decisions, not the model's mood. What you'll see ย โข End-to-end walkthrough of Omni Agent: goals, plans, tasks, ledgers, telemetry ย โข A real scenario on a codebase (e.g. an Angular chat app)โ-โfrom "investigate this" to concrete actions and tracked outcomes ย โข How decisions, capabilities, contracts and context are modeled in DDSE & ACM ย โข Architecture view of Omni Agent as a "Capability OS": planner, executor, context layers and extensibility ย โข Honest trade-offs: what is still weak, what's missing, and where this approach may or may not fit your environment Who this is for ย โข Engineering leaders and architects evaluating agentic platforms ย โข Developers who want more than "prompt + tools" and care about system design ย โข Anyone worried about the AI bubble and looking for deterministic, governable AI systems Format ย โข ~40 minutes of platform demo + design walkthrough (via YouTube Premiere) ย โข I'll be present live in the chat ย โข Follow-up Q&A thread on LinkedIn for deeper questions
Most โKnowledge basesโ today are just vector indexes with a chat UI. Without the LLM, they know nothing. With the LLM, every answer re-rents the same knowledge in tokens.
๐๐ฒ๐ ๐๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐๐: - A vector store isnโt a knowledge base; itโs a smart memory. The โknowledgeโ lives in the model you keep paying to re-read your own documents.
- Without a model (entities + relationships), you lock in two long-term costs: high tokens per question and shallow answers per question.
- A lightweight knowledge model lets you store facts once, query them cheaply, and use the LLM only for judgment and languageโโโnot for rediscovering the same truths forever.
๐ช๐ต๐ฒ๐ป ๐๐๐ฒ๐ฟ๐๐ผ๐ป๐ฒ ๐๐ ๐ฎ๐ป ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐ ๐๐ฐ๐ธ ๐๐ฆ ๐๐ค๐ข๐ญ๐ฆ ๐๐ฑ ๐๐จ๐ฏ๐ฐ๐ณ๐ข๐ฏ๐ค๐ฆ ๐ช๐ฏ ๐๐ฐ๐ง๐ต๐ธ๐ข๐ณ๐ฆ Published on Medium in AI Advances Publication| Nov 20
This one is for teams where everyone suddenly carries the hashtag#architect label and every deck has an LLM box in the middle. My new piece, โWhen Everyone Is an Architect,โ is a small reality check on how we build software and AI platforms now: more diagrams than foundations, more confidence than discipline. If that sounds uncomfortably familiar, you might enjoy it.
