Aimagutchi
A persistent AI agent—with skills you can reuse next session
Prosumer builders waste tokens when agents behave like fresh chats. Persistence means context, history, and playbooks that compound.
The pain
- Agents reset between sessions and lose hard-won context.
- You cannot see what the agent did last time or which skills it used.
- Recurring jobs feel like onboarding the same bot again and again.
Why chats & prompts fail
- Most “agents” are prompts wrapped in UI—not systems with durable state.
- Without history, trust and reuse never form.
- Blank-canvas setup slows first value and hides whether persistence works.
Persistent memory + playbooks
- Adopt a seeded agent with purpose, memory, and a ready workflow.
- Inspect chronological runs with summaries and timestamps.
- Rerun the same template with saved context instead of rebuilding from scratch.
First win in minutes
Day-7 starters ship with seeded memory, sample history, and one reusable workflow—so you see continuity before you build from scratch.
Meet a persistent starter agentYour starter
Atlas Owl is ready
Memory, work history, and one reusable workflow — no API keys required.
Saved memory
- Brief format preference
Verdict → evidence → next action
Work history
Compared three note-taking tools for long-term retrieval quality
Memory reused on last run
Verdict: prioritize visible history + reusable skills in week one…
Research brief workflow
Run sample35% fewer tokens vs baseline12 skills saved
FAQ
- What makes an agent “persistent”?
- Durable memory across sessions, visible work history, and reusable workflow templates that reload context instead of starting cold.
- How fast is first value?
- Signup → choose a starter → run sample input. Most users see useful output and memory reuse proof within minutes.
Stop rebuilding. Start compounding.
Create a free account, pick a starter, and see memory + history on your first run.
Meet a persistent starter agent