Grigoriy Dobryakov – IT+AI Blog
Grigoriy Dobryakov's blog: management, development and testing
AI keeps repeating the same mistakes? That’s not a model problem. It’s an engineering gap. How to give your agent persistent memory in three minutes — and why this beats RAG for most projects.
Developers complain that AI repeats the same mistakes. "I've explained five times that we don't use Redux here." "It suggested FTP again instead of a queue." "It keeps ignoring our naming conventions."
That's not a model problem. It's an engineering gap.
Continue reading “The Self-Learning Agent Pattern: decisions.md + mistakes.md”
‘AI is just a very fast intern coder’ is an accurate description of stage one. At stage two, AI becomes an executor of thinking at every step of the SDLC — from clarify dialogs with stakeholders in Jira to multi-agent pipelines that catch, fix, and verify themselves.
"Writing code is only a small part of the SDLC. There's also stakeholder interviews, requirements formulation, architectural design, testing, monitoring. AI is just a very fast coder — not a thinking member of the team."
That argument is right. For stage one.
At stage one, someone watches AI write code and concludes: "AI = coder." Then they notice that coding isn't the whole of software development. "Therefore AI won't replace engineering entirely." The logic holds. The conclusion doesn't.
At stage two, AI covers exactly what that argument says is missing.
Continue reading “AI Is Not Just a Coder: How AI Covers the Entire Software Development Lifecycle”
Hype says “hire AI.” Job descriptions buy something else: production agents with evals and guardrails, Applied AI leadership, partner architecture, evaluation harness as a must-have. Method: read role clusters as an org chart under pressure.
Hype says “hire AI.” Real job descriptions buy people who own production agents, evals, Applied AI, and partner delivery. Below is a method for reading vacancy clusters as an org chart under pressure, plus five role clusters from a parsed corpus of open roles.
Continue reading “AI Job Demand in 2026: What Hiring Signals Show in Job Postings”
Three skills are fine. Thirty — style drifts, shared state gets overwritten, behavior changes with no stack and no log line. Dropping markdown into `.claude/skills/` is a hire with no interview. Onboarding exists so you catch the conflict before it hits daily flow — not because process looks nice.
Because copying markdown into a folder is not installing a package. It is a hire: a new slice of behavior enters the assistant and will collide with its neighbors tomorrow. A skill here is an instruction pack for an AI assistant (Claude Code / Cursor): frontmatter, rules, declared reads/writes/calls. While the intake path is empty, every new file under .claude/skills/ is a silent hire with no interview and no record in the file.
Continue reading “Why AI skills need onboarding if you can just copy them into the folder”
“Don’t delete prod” in a system prompt is not a security control. The real boundary is where the model physically cannot act: API surface, isolation, operation filter, audit. Working reference: enterprise-code-bastion.
A prompt is a wish. A perimeter is when the model physically cannot reach files or a shell: native tools off, an MCP intermediary, Docker per project+task, a filter before execution, an audit trail. Below is a working reference — and where the design gives way.
Continue reading “How to secure an AI coding agent without relying on the system prompt”
