Nice point, the idea that agents shine only after intent is clear really resonates. Makes me rethink where I actually expect time savings from AI in real projects.
Agentic Workflows in Software Engineering
Valentine Shi
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— Originally published at valentineshi.dev
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Valentine Shi
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@[Kevin Ryker] Agree, Kevin. Provided the AI usage patterns are only emerging, estimating AI time saving reliably is hardly possible. Only "gut feelings".
We only can time-estimate something that has a repeatable behavior pattern. For AI usages in software engineering - hardly predictable domain = it is, probably, a farther future.
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Senior Backend / Full-Stack / Founding Engineer specializing in complex business systems. Node.js, T... Show moreSenior Backend / Full-Stack / Founding Engineer specializing in complex business systems. Node.js, TypeScript, LLM Decision Workflows and AI-augmented accelerated product development.
I own - design and build production backend systems end-to-end in collaboration with product and engineering teams: from requirements, system architecture and contract-first APIs (OpenAPI) to ingestion pipelines, async orchestration, deployment, observability.
I actively use AI-augmented development workflows and spec-driven engineering to accelerate delivery while preserving the code validity and effectively minimizing defects. I design and implement AI/LLM programmatic decision workflows with constrained outputs, controlled vocabularies, and deterministic validation to ensure reliable behavior and eventual correctness in systems.
I ship high-reliability, low-firefight backend platforms for startups and early scale-ups, from day one built to be easily evolvable and fully prepared for continuous product change.
I use the following tools for that:
- Extended Model-Based Engineering (C4, UML/PlantUML for domain, architecture and fine sequence/state modeling)
- Domain-Driven Design (DDD) with Hexagonal Architecture
- Contract-First APIs (OpenAPI, AsyncAPI, JSON Schema validation, generated contracts enforcement)
- ATDD/TDD/E2E (Specification-by-Example, data providers, Testcontainers, integration-first backend testing)
- Event-driven and async workflow architectures (webhooks, queues, idempotence, state-based orchestration workflows)
- Deterministic automated code quality gates (linting, static analysis, git hook guards in CI, ~100% code coverage)
- Competent AI-augmented product engineering: OpenSpec SDD, agentic workflows, rapid prototyping, legacy refactoring, vibe-coding remediation, explicit engineering introduction
See my public engineering case: AI-Powered Image Generation & Publication System (Imagetron) at: https://valentineshi.dev/content/deliverables/K3aT7UX_RCC8ZO_fy9VinQ/ai-powered-image-generation-publication-system-imagetron
More details and other delivered public cases: https://valentineshi.dev Show less
I own - design and build production backend systems end-to-end in collaboration with product and engineering teams: from requirements, system architecture and contract-first APIs (OpenAPI) to ingestion pipelines, async orchestration, deployment, observability.
I actively use AI-augmented development workflows and spec-driven engineering to accelerate delivery while preserving the code validity and effectively minimizing defects. I design and implement AI/LLM programmatic decision workflows with constrained outputs, controlled vocabularies, and deterministic validation to ensure reliable behavior and eventual correctness in systems.
I ship high-reliability, low-firefight backend platforms for startups and early scale-ups, from day one built to be easily evolvable and fully prepared for continuous product change.
I use the following tools for that:
- Extended Model-Based Engineering (C4, UML/PlantUML for domain, architecture and fine sequence/state modeling)
- Domain-Driven Design (DDD) with Hexagonal Architecture
- Contract-First APIs (OpenAPI, AsyncAPI, JSON Schema validation, generated contracts enforcement)
- ATDD/TDD/E2E (Specification-by-Example, data providers, Testcontainers, integration-first backend testing)
- Event-driven and async workflow architectures (webhooks, queues, idempotence, state-based orchestration workflows)
- Deterministic automated code quality gates (linting, static analysis, git hook guards in CI, ~100% code coverage)
- Competent AI-augmented product engineering: OpenSpec SDD, agentic workflows, rapid prototyping, legacy refactoring, vibe-coding remediation, explicit engineering introduction
See my public engineering case: AI-Powered Image Generation & Publication System (Imagetron) at: https://valentineshi.dev/content/deliverables/K3aT7UX_RCC8ZO_fy9VinQ/ai-powered-image-generation-publication-system-imagetron
More details and other delivered public cases: https://valentineshi.dev Show less
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