To replace the mangers, the AI needs to take care of the following pain points (got some help from chatgpt): Hiring and Talent Acquisition: - Candidate pipeline quality is inconsistent. - Time-consuming resume screening and interview scheduling. - Lack of diverse candidate pools.

Performance Management - Biased or inconsistent performance reviews. - Goal-setting lacks clarity or alignment with org OKRs. - Lack of real-time performance insights.

Project Planning and Execution - Estimations are often inaccurate. - Project scope creep due to unclear requirements. - Dependencies across teams delay execution.

Technical Debt and Code Quality - Mounting technical debt slows velocity. - Inconsistent coding standards. - Hard to trace ownership for legacy code.

Team Collaboration and Communication - Cross-team communication breakdowns. - Time zones complicate decision-making. - Meeting overload or lack of clarity post-meeting.

Onboarding and Knowledge Transfer - New hires take too long to ramp up. - Tribal knowledge isn't well documented. - Onboarding processes are inconsistent.

Incident Management and Reliability - Blameless postmortems are rarely actionable. - Alert fatigue from noisy signals. - Root cause analysis (RCA) is time-consuming.

Career Growth and Mentorship - Lack of clarity in career ladders. - Mentorship is ad-hoc and inconsistent. - Managers don’t have enough time for coaching.

Engineering Productivity Metrics - Metrics often feel punitive or misused. - Hard to attribute impact to engineers’ work. - Lack of actionable insights from engineering data.

Cross-Functional Alignment - Product and engineering priorities are misaligned. - Specs often change mid-cycle. - Lack of visibility into roadmap tradeoffs.

Each of these categories will probably need an AI agent in itself and probably an AI agent to control all the other AI agents. It would be a complex system, but will still need some monitoring until it is self-sustaining.