The New Factory
Systems built for automation from day one.
Agentic AI - Current State
Agentic / Agent vs Autonomous AI
Agentic / Agent
AI that chooses its own tools, plans steps, and orchestrates workflows. You give it a goal; it figures out the how. A human starts it.
Example: Claude Code reading your codebase, deciding which files to edit, running tests - but you told it what to do.
Autonomous AI
Fully autonomous AI that acts on triggers or schedules without human initiation. It runs in the background, watching for events.
Example: An agent that polls Azure DevOps every 30s, claims tickets tagged “CLAUDE,” executes work, and closes them.
The magic isn't the agent - it's the process it's embedded in.
Tool Selection Is Still Just Text
The surprising truth: when an agent “decides” to call a tool, it's not executing code - it's generating text that describes what it wants to do. The system interprets that text and runs the tool.
What “Autonomous” Looks Like
CoWork Scheduled Tasks
- •Set cadence: hourly, daily, weekly, weekdays, on demand
- •Each task = its own session with all files + plugins
- •Desktop app only, paid plans
Dispatch
- •Send tasks from phone → Claude executes on desktop
- •Scan QR code → connected in 2 taps
- •Note: tasks are routed through Anthropic’s cloud — not fully local processing
Claude CLI (Claude Code)
- •Terminal-based agentic coding tool (GA May 2025) - human-driven, not autonomous
- •Natural language → code, file ops, git, shell, web search
- •Autonomous only via /loop (recurring tasks) or scheduled triggers
These tools don't just answer questions - they DO things. Browse the web. Open files. Write code. Send emails. All autonomously, with permission controls.
Parallel research with Claude Code
Running multiple research tasks in parallel from the CLI - real-time autonomous execution.
Demo: Automated Lead Research Pipeline
Fully autonomous sales pipeline
End-to-end: Google Form intake → CSV → CoWork scheduled job picks up new entries → AI researches each company → saves research → second job generates personalized outreach emails. Zero manual work.
Result: Lead intake → personalized email draft - human only reviews and sends.
Google AI Studio App with Database
Link the CSV from the lead pipeline into Google AI Studio. AI builds a full app to browse leads, view research, edit outreach drafts. Built entirely for free - no code.
Don't Ask AI to DO Finance - Ask It to BUILD the Tool
AI makes mistakes. Math doesn't. The solution: use AI to build a deterministic app that executes reliably every time.
Instead of asking Claude to calculate your KPIs, ask it to build an app that pulls data from Azure DevOps, calculates the KPIs with exact formulas, and shows a dashboard. The app is reviewable, testable, and repeatable.
Wrong approach
“Calculate our sprint velocity and defect rate from this data.”
AI may hallucinate numbers, round differently, miss edge cases.
Right approach
“Build me an app that connects to Azure DevOps, fetches work items for the last 6 sprints, and calculates velocity and defect rate using [these exact formulas].”
Deterministic. Reviewable. Repeatable.
Azure DevOps KPI automation
Using Claude Code / AI Studio to build an app that pulls Azure DevOps data and generates KPI dashboards - deterministic code, not AI guesswork.
Connecting Claude to your tools
MCP servers, connectors, and app integrations - giving Claude access to email, databases, and APIs.
Building a production agent
Using the Claude Agent SDK to build an autonomous workflow with tool access and human-in-the-loop controls.
The Human as Operator
| Model | Human Role | AI Role | Analogy |
|---|---|---|---|
| Human-in-the-loop | Does the work, AI assists | Copilot/assistant | Worker with power tools |
| Human-on-the-loop | Monitors, intervenes on exception | Autonomous executor | Factory floor manager |
| Human-out-of-the-loop | Sets policy, reviews outcomes | Fully autonomous | Business owner reviewing reports |
The Klarna Rollercoaster
AI Deployed
2.3M conversations/month. Equivalent to 700 full-time agents. Satisfaction up 47%.
Full Speed Ahead
Headcount 5,000 → 3,000. $10M annual savings. Response time: 15 min → under 2 min.
Complex Cases Fail
Quietly rebuilding human customer service team. Full AI replacement failed for sensitive cases.
Hybrid Model
Human-on-the-loop stabilized. AI handles volume, humans handle complexity.
The Klarna Rollercoaster
2.3M conversations/month, 700 agents replaced - then they reversed course.
The process guards quality, not the individual. Review gates, validation steps, automated checks.
Real performance gains come from process redesign, NOT AI “assisting” the human.
Every factory has a dark side. Let's talk about what happens when you build without guardrails.
Enter the shadow factory →