A minimalist design showing interconnected nodes or workflow paths representing AI agents and business automation.

Gnothi Agentic Business

by OCDevel

7 episodes · updated 10 Sep 2026

Technology

Put AI agents to work on a business customers have a reason to pay for. This practical course follows an illustrative business from customer research and a useful offer through a software product, sales, delivery and recurring operations. Learn to give an agent sources and a clear brief, inspect its output, and turn a useful session into instructions and routines you can reuse. General assistants and coding agents receive sustained attention across research, programming, documents, spreadsheets and connected business tools. As the business develops, the lessons bring in marketing, generated images and video, customer acquisition, payments, bookkeeping and company administration. AI concepts enter where they help explain an output or diagnose a failure. As delegation expands, learn to monitor the operation through completed tasks, reliable records, costs and exceptions that need your judgment. For solo founders, freelancers and small-business operators comfortable with a computer, business and technical foundations are taught as they become useful. News examines tool changes that affect these workflows.

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1. What Delegation Leaves Behind

The chapter tests a trade-business idea on invented research notes, first through ordinary chat and then through a properly built project with sources, a strict brief, and saved instructions. The comparison exposes an unsupported statistic and leads to a rewritten process that ends in honest unknowns rather than false confidence. A closing section explains the difference between the model, the application holding it, and a tool-using agent.

2. Driving a Project Workspace

How a Claude project assembles what it sends, with instructions prepended and paid for every turn, chats that share no memory, documented file limits worth re-checking, and the switch from direct context to retrieval over chunks; plus the note schema, filename rules, and four portable questions, put to ChatGPT Projects, Gemini Gems and NotebookLM from their September 2026 help pages.

3. Turning a Good Procedure into a Skill

The chapter moves evidence-checking rules out of copied project instructions and into a single Claude skill that can load for any job that needs it. The first test shows none of the skill's steps, which points most likely to a missed trigger. After fixes to the description and one rule, the skill produces three evidence-backed candidate offers with their gaps marked.

4. Choosing Where the Work Runs

The chapter sorts AI tools by where a job's files live, what the job must touch, how it starts, and how its result gets checked, rather than by product label. It then uses that rule to set up the enquiry digest as a scheduled Claude Code task over invented data, which exposes both a hidden gap and the real limits of unattended runs.

5. A Narrow Offer With a Written Boundary

The chapter chooses the end-of-day enquiry digest as the offer, writes its scope and five checkable acceptance criteria, and works out an illustrative cost floor dominated by review time. It then tests Claude Code, ChatGPT and NotebookLM against the same criteria, sets Perplexity aside, and keeps the current setup for concrete reasons.

6. Putting a Price on the Digest

The chapter turns an invented cost floor into a testable first price, builds a self-checked sample digest, and designs owner interviews that count commitments rather than praise. It then connects Claude Code to a Google Drive folder with three separate read-only limits and verifies what the agent actually did.

7. Asking for the First Commitment

Generating…