Corrections That Stick
Self-modifying agents, and how to verify a correction survived the night.
TUTOR WITH THEFOCUS.AI
Copy this prompt into Claude, ChatGPT, or any external AI assistant. It points the assistant to the course instructions and links it to your student profile to track your progress and customize observations.
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Interact, correct, verify
Start a chat session with your agent:
umwelten chat bookkeeping-agentAsk a real question. Make a correction. End the session. Start a new session. Verify the correction persisted.
Cornwall Market: Sarah starts a chat with the bookkeeping agent and asks about a $600 invoice from Chen’s Produce.
The agent categorizes it correctly — account 5100. Sarah says, “Actually, any Chen’s Produce items over $500 should be flagged for manager review. We had a case quantity error last quarter and I want to catch those early.”
The agent acknowledges the correction and updates its categorization skill — adding a new exception rule. Sarah ends the session.
She starts a new session the next day. “I have a $720 invoice from Chen’s Produce.” The agent categorizes it to 5100 and flags it for manager review, citing the rule Sarah added yesterday. The correction stuck.
Run umwelten chat your-agent. Ask a real question from your domain. Then make a correction — tell the agent something it should do differently next time. End the session. Start a new one. Ask a question that should trigger the correction. Did it persist?
If you enabled self-modify in your habitat config, the agent updated its own skill files. Check the skill — you will see the correction recorded. This is the feedback loop in action.
One agent, many windows
The same habitat, with the same tools and the same memory, is accessible from any interface:
| Interface | How It Works | Best For |
|---|---|---|
| CLI | umwelten chat bookkeeping-agent | Interactive development, debugging, corrections |
| Telegram | Message the bot directly | Mobile access, quick checks, approvals |
| Discord | Channel or DM | Team collaboration, shared visibility |
| Web dashboard | Open in browser | History review, team access, management |
The interface is a surface. The agent underneath is the same. A correction you make on Telegram is reflected when you open the CLI. A decision logged in the web dashboard is available in Discord. One agent, many windows.
Every conversation is a transcript.jsonl file — searchable, replayable, exportable. When the agent picks up a conversation tomorrow, it loads the transcript and has full context. When you want to audit what the agent did last Tuesday, you search the transcripts.
Multi-provider strategy
Umwelten supports multiple providers through a single getModel() interface:
import { getModel } from 'umwelten';
const claude = getModel('claude-sonnet-5');
const gemini = getModel('gemini-3.5-pro');
const local = getModel('ollama:qwen3');
// All return the same interface — swap freely
const response = await claude.generate(prompt, { tools });Same interface, any provider. The eval system uses this to test across providers. The production system uses it to route requests. Same code, different purposes.
Check your understanding
Answer in your own words — write it down before opening the key. Your tutor grades against the criteria and generates fresh variants on retries.
Q1. In a habitat, what makes a correction persist across sessions, and how would you verify tomorrow that a correction you made today actually stuck?
Answer key — attempt every question first
Answer key
Q1
Model answer: Persistence comes from the correction being written into the habitat’s files — the skill or memory files the agent loads at startup — not merely said in a conversation. Verification: start a fresh session tomorrow, pose a case that triggers the rule, and confirm the agent applies it; optionally check the skill file’s diff to see the correction recorded.
Pass criteria: file/memory write as the persistence mechanism; verification = fresh session + triggering case (and/or inspecting the diff)
Next: From Eval to Production