AgenticWerx

As agents begin owning more of the incident lifecycle, clear guardrails and predictable behavior will matter as much as raw automation.

Engineering teams are moving faster than ever with AI coding tools. But speed comes with a new challenge: How do you keep your team's standards intact when AI is generating a growing percentage of the code? This is where AI governance for engineering standards becomes critical. When teams define clear rules around: → Patterns → Validations → Security checks → Architectural boundaries → Consistency requirements AI becomes a force multiplier instead of a source of drift. Strong governance doesn't slow teams down — it boosts productivity by: → Reducing PR friction → Cutting down on repeated review comments → Keeping code consistent across contributors → Preventing avoidable mistakes → Making onboarding smoother AI generates the code. Governance ensures it reflects your team's standards. Question for engineering leaders: How does your team maintain consistency as AI becomes part of the workflow? #SoftwareEngineering #DevOps #AICode #CodeQuality #Productivity #PlatformEngineering

Code review is supposed to catch issues early. But in many teams, it has quietly become one of the most expensive parts of the development cycle. Why? - repeated comments on the same issues - inconsistent expectations between reviewers - senior engineers overwhelmed with review volume - AI-generated code increasing the number of PRs Teams relying on tribal knowledge instead of clear standards The challenge isn’t review itself — it’s the lack of consistent, enforceable rules across the team. When every reviewer has a different definition of “good,” PRs take longer and quality drifts. How does your team keep review time under control? #softwareengineering #codereview #devops #platformengineering #aiassistants #softwarequality #securecoding #softwarearchitecture #engineeringmanagement #cicd #automation #developers

With AI-generated code entering everyday development workflows, one trend is becoming clear: Engineering standards matter more than ever. AI tools can accelerate development, but they can also introduce: - inconsistent patterns - missing validations - security gaps - architectural drift Teams need a reliable way to define and enforce their own internal rules — not just rely on generic linters or formatting tools. How has AI impacted code consistency in your team? #softwareengineering #devops #platformengineering #aiassistants #codequality #securecoding #softwarearchitecture #engineeringmanagement #automation #cicd #developers #aicode

We built AgenticWerx because we kept seeing the same problem: AI coding assistants are powerful. But they don't know how YOUR company builds software. They suggest one auth pattern. Your team uses another. They generate generic error handling. You have specific requirements. They create inconsistent code across developers. You need standards. Your engineering standards exist—buried in Confluence, Notion, wikis, and PDFs. But AI can't use them. So we built a solution: Upload your standards once → AI agents have access automatically. Works with Amazon Q Developer and Kiro IDE. Setup takes 5 minutes. Early results from teams using AgenticWerx: • Up to 60% faster onboarding for new developers • Up to 40% less time in code reviews • Consistent patterns across all teams We're live with three plans: • Basic (Free) - Get started with 2 custom rules • Pro (29/mo)−10 custom rules for individual developers• ProPlus(299/mo) - 200 shared rules for teams up to 15 members If you're adopting AI coding assistants and want them to follow your company's way of building, check us out: agenticwerx.com #AI #SoftwareEngineering #DeveloperTools #EngineeringStandards