Observed Signal · Apr 25, 2026 · Technical Guide · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Agentic AI Workflows for Platform Engineering
This is Part 1 of a technical series explaining how to build agentic AI workflows for platform engineering teams. The author argues that improving developer velocity requires encoding team standards into the workspace (not just better prompts): steering files, skills, and agent definitions that provide persistent, role-specific context for AI agents. The post outlines a layered workspace model (e.g., a .kiro/ directory) that injects non-negotiable rules into every AI interaction, describes specialised agents for tasks like infrastructure authoring and security review, and details tool integrations (ticket trackers, CI/CD, AWS). The assumed stack includes AWS (multi-account), Terraform, GitLab CI, and AWS Secrets Manager. The article provides immediate starter steps (create a steering file and AGENTS.md) and previews later parts covering detailed steering files and GitOps/Kubernetes tooling.
Practical technical guidance on embedding agentic AI into engineering workspaces can influence developer productivity and tool design, but it's a how-to blog post rather than a major platform policy or product launch.
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Key Takeaways & Evidence Grounding
- Article is Part 1 of a series about building agentic AI workflows for platform engineering teams.
- Primary recommendation: encode team conventions into workspace steering files so AI agents automatically follow project-specific rules.
- Assumed tech stack: AWS (multi-account, Control Tower for landing zone), Terraform for IaC, GitLab for source control and CI/CD, and AWS Secrets Manager for secrets.
- Tooling choice highlighted: Kiro (an AI-powered IDE) using a .kiro/ layered context model (steering files, skills, agent definitions); AGENTS.md is offered as a portable fallback.
- Planned later coverage: steering files details, Kubernetes (EKS), Backstage developer portal, and GitOps with ArgoCD.
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Developer’s Practical Workflow for Working with AI Agents
Mitesh Sharma published a first‑person account on DEV Community (2026-06-16) describing how he uses AI agents in software development. He argues that planning, architecture and test strategy are now more important than hand-coding because agents can execute tasks quickly but will follow vague plans incorrectly. His workflow: design a clear plan, decompose work into small independent tickets, have an agent implement a ticket, use a different model to review the code, and require human review only for high‑risk changes. He stresses enforcing non‑negotiable rules (via hooks, CI checks or scripts) rather than relying on natural‑language instructions, documents architecture rules for agents to follow, and iteratively improves the surrounding “harness” (skills, guardrails, review workflows) to increase long‑term value.
Make AI Skills Persistent for Agentic Workflows
The article explains that AI "Skills"—small, shareable files that codify procedures—have shifted from a personal prompting shortcut to an organizational, agent-invoked standard. Anthropic added Skills into Excel and PowerPoint sidebars on March 11; the skills format has been adopted across vendors (OpenAI, Microsoft, GitHub, Cursor) and the author says ~500,000 skills now run interoperably. Key changes: agents call skills autonomously, admins can provision skills across organizations, and the same skill files run in developer terminals and productivity apps (M365). The author outlines architectural patterns (progressive disclosure, specialist stack, orchestrator), explains why conventional skill design fails for agentic use, prescribes five elements every skill body needs, and offers four prompts and practical tests to make skills agent-ready. The piece also includes access to a skills repository and team-deployment guidance.
From Prompt Engineering to Agentic AI Systems
This engineering-focused blog post explains Agentic AI—autonomous systems that understand objectives, plan, select tools, execute tasks, observe results, and iterate until goals are met. It defines the four essential building blocks for production agents (Brain/LLM, Tools, Memory, Goal), describes the ReAct Think→Act→Observe loop, and emphasizes planning, memory, observability, and error handling for reliability. The author gives a short code example using LangChain and ChatOpenAI, discusses multi-agent architectures and specialized agent roles, compares orchestration frameworks, and lists an engineering stack of frameworks, vector stores, and infrastructure components used to build autonomous AI systems.
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