AI in the SDLC becomes genuinely interesting the moment an assistant stops suggesting and starts participating in the working loop as an executor.
The chapter shows how agent workflows reshape development tooling through protocols, access control, evaluation, and the practical limits of adoption inside a real team.
In design reviews, it helps you discuss autonomy boundaries, the trust model, and AI's place in engineering work without drifting into vague talk about the future of software development.
Practical value of this chapter
SDLC evolution
The chapter helps you show how AI moves from suggestion mode into active participation inside the development loop.
Autonomy boundaries
It is a practical way to discuss which actions an agent can be trusted with and where a human must remain in the loop.
Measured impact
The material shows why SDLC AI cannot be discussed through speed alone without quality, safety, and rollback cost.
Interview material
It is a strong case for discussing AI in the SDLC through protocols, access control, evaluation, and organizational constraints.
AI in SDLC: the path from assistants to agents by Alexander Polomodov
An extended talk about how engineering teams move from AI assistants to agent workflows and rebuild development processes around what the speaker calls Software Engineering 3.0.
Source
Telegram: Book Cube
The main post for the report with the structure of topics and the context of the speech.
Key trajectory
Assistants: copilot mode in the IDE
The first wave centered on autocomplete, template generation, and faster local development.
Shift to agents
An agent works against a goal: it plans steps, invokes tools, operates on the repository, and returns a measurable result.
From standalone tool to platform
This requires shared protocols and a common infrastructure: context, security, audit, access control, and standardized integrations.
Software Engineering 3.0
The SDLC shifts toward a model where a human sets intent, an agent executes, and a human validates the result before deciding whether to release changes.
Agent scenarios in platform practice
- Agent mode in product development, demonstrated on the “5 Letters” game case.
- Agent in a Python notebook for data work and faster analytical loops.
- Agent for testing and test-scenario generation with an emphasis on risky cases.
- Agent for code review: finding defects, code smells, and standards violations.
- Vulnerability-detection agent (safeliner) inside a secure SDLC loop.
Related chapter
Programming Meanings by Alexey Gusakov (CTO Yandex)
A shift toward intent-driven development and connected product/ML cycles.
Infrastructure and economic drivers
Economics of agency
Reducing compute costs and increasing quality of foundation models make multi-step agent scenarios practical.
Integration protocols
MCP and A2A approaches reduce the cost of connecting tools and simplify the orchestration of agent-to-tool and agent-to-agent flows.
Tool base
Tooling is already shifting toward specialized agents and CLI-based workflows, for example Claude Code and OpenAI Codex.
Management and regulation of agency
- An agent-governance model has to define autonomy levels and decision boundaries.
- Critical actions such as security work, production configurations, and migrations require a human in the loop and traceable approval.
- Result evaluation must cover not only speed but also quality: defects, vulnerabilities, and rollback cost.
- Agent metrics become part of the engineering platform and the management loop around it.
Related topic
Observability & Monitoring Design
How to build a measurable feedback loop for production platforms.
Measuring the effectiveness of assistants and agents
Productivity
Lead time, cycle time, throughput, time to first pull request
Quality
Defect escape rate, rework rate, flaky tests, review findings
Reliability and safety
Security findings, policy violations, rollback rate, incident impact
Adoption
DAU/WAU, retention, share of tasks handled with agents, reasons for opting out
Links and materials
YouTube: AI in SDLC
Full recording of the talk.
VK Video
An alternative platform for viewing the episode.
Telegram: main post
Talk structure, links, and the main takeaways.
Previous report
An introductory framework for integrating AI into development processes.
Selection of sources (part 1)
A curated set of links on agents, case studies, and engineering practices.
Selection of sources (part 2)
Continued materials for in-depth study.
AI survey in development
Study of the influence of AI on software engineering in Russia.
Related chapters
- AI Engineering - Production practices for AI lifecycle, quality loops, and operational control.
- Prompt Engineering for LLMs - Intent and constraint design for predictable assistant and agent behavior.
- Programming Meanings by Alexey Gusakov (CTO Yandex) - A complementary intent-driven view of product and ML development cycles.
- Observability & Monitoring Design - Metrics, alerting, and feedback loops for AI features in production.
- Testing Distributed Systems - Reliability-testing approaches for complex systems and risky rollouts.