Prepare Yourself
Own workflows, govern autonomy, protect identity & data, and profit in the transition.
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Shadow your role (10 days)
Set up a daily journal and pair with an AI agent to mirror your core tasks.
Each evening record agreement rate, time saved, and any corrections in your journal.
Why: You measure AI accuracy and efficiency, build confidence over time, and identify improvement areas.
How: Use a simple spreadsheet or app with columns for Task, AI Output, Review Score, Time Saved, and Notes — this feedback loop teaches the agent your preferences.
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Productize tasks
Identify three or more repetitive tasks each week; for each, create prompt templates, validation checks, and test scripts; maintain version history.
Why: You transform ad-hoc work into modular assets that can be reused, licensed, or scaled across teams.
How: Document each task’s objective, input data, expected output, and acceptance criteria in a template repository so any agent or team member can execute it precisely.
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Manage autonomy
Define clear performance thresholds (e.g., ≥90% accuracy), set escalation rules for failures, and create review dashboards.
Why: These guardrails ensure AI stays within acceptable bounds and human oversight can intervene when needed.
How: Use BI tools (e.g., Grafana, Power BI) to visualize task performance metrics, configure alerts, and schedule regular check-ins to adjust thresholds.
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Harden identity & data
Enable FIDO2 passkeys and hardware security keys, encrypt all sensitive vaults, create offline backups, and rotate credentials quarterly.
Why: Strong authentication and encryption protect against unauthorized AI access or data leaks.
How: Follow zero-trust principles: require multi-factor for all logins, encrypt at rest/in transit, and track all keys in a secure inventory that gets audited regularly.
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Sandbox → Spend
First run agents in read-only mode to review proposed actions, then validate outputs, and finally enable budget and time-window controls (e.g., $500/month cap, active only 9 AM–5 PM).
Why: This staged approach prevents costly mistakes and ensures you trust the agent before granting real-world permissions.
How: Configure policy rules in your agent platform: phase 1 = report-only, phase 2 = review-required, phase 3 = live execution with limits.
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Personal context graph
Tag your documents, decisions, and contacts with semantic labels (e.g., “ProjectX”, “VIP”) and store this in a privacy-first graph database.
Why: Agents need structured context to reason effectively about your world, tasks, and priorities.
How: Define entity types and relationships, assign privacy levels, and expose a secure API endpoint that agents can query for context during execution.
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Kill‑switch runbook
Document step-by-step procedures to revoke tokens, pause all automations, and rollback recent changes. Store the runbook offline and rehearse at least quarterly.
Why: In the event of an AI misbehavior or system failure, this ensures you can immediately halt operations and recover safely.
How: Create a versioned runbook in your CI system, distribute printed copies to key personnel, and conduct simulated drills to validate the process.
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Publish proof‑of‑work
Compile your before-and-after metrics into a concise 1–2 page case study and share it publicly or with friends, family, etc.
Why: Demonstrating real performance gains builds trust, attracts investment, and accelerates adoption.
How: Structure your case study with clear objectives, methodology, results (quantified), and key lessons learned, then distribute via blog posts, presentations, or newsletters.