What is the AI agent lifecycle?
The AI agent lifecycle covers four stages: forge (define purpose, tools, boundaries in natural language), train (decisions personalized through user approval/denial, written into a Bayesian profile and LoRA adapter), publish (the matured agent becomes a cloud API), and tether (every production call still flows through your supervision layer). The agent is born on the desktop, raised under your watch, and continues to learn after launch.
01Kimler için?
The lifecycle approach speaks to three audiences:
- Developers building AI capability into their own product. Customer support, document automation, internal tooling.
- Professionals delegating recurring work. Email triage, meeting prep, report generation.
- Small teams who need shared, auditable agents. Internal knowledge, consistent decision rules, compliance trail.
02Nasıl çalışır?
Four stages, in sequence:
- Forge. You describe the agent in natural language — what it does, which tools it can use, what limits it must respect. No code, no YAML.
- Train. The agent runs in a sandbox. Each risky action is presented to you; you approve or deny. Decisions are written to a Bayesian decision profile and into a LoRA adapter via DPO (direct preference optimization). The agent starts deciding the way you would.
- Publish. The matured agent is pushed from desktop to a cloud runtime. Your product calls
POST https://api.ilura.com.tr/v1/agents/:id/chatfrom anywhere. - Tether. Every production call still flows through your policy + audit + learning layer. Each morning a digest lands on your desktop ("yesterday I made 2,400 decisions; 17 wanted to ask you, 3 were risky"). You review; the loop closes.
03Ilura ile nasıl yapılır?
In Ilura, the lifecycle is the product:
- Forge in the Tezgâh (workbench) scene. Click "+ New agent", describe it in one sentence, attach tools through MCP (Model Context Protocol).
- Train through use. Every tool call surfaces as an approval, denial, or biometric prompt depending on risk level. No separate dataset preparation.
- Publish in one click. Once Bayesian confidence and eval scores cross a threshold, the publish button activates. The agent definition + LoRA adapter + memory snapshot ship to
api.ilura.com.tr. - Tether stays alive. Production audit events stream back to your desktop asynchronously. The relationship doesn't end at publish — it deepens.
04Sık sorulan sorular
How is this different from typical AI agent platforms?
Most platforms cover one or two stages — building, or memory, or hosting. Ilura covers all four and connects them. The same agent that lives on your desktop is the one running in production, and learning at both ends.
Do I need to write code?
No. Forge, train, and publish are natural-language flows. Code matters when integrating the published API into your own product — Python, TypeScript, curl, anything that speaks HTTP.
Where does training data live?
On your machine. Local SQLite database, SHA-256 hash chain integrity. Only published-agent production traffic touches the cloud runtime.
How does the tether differ from a webhook or audit log?
A webhook fires once and is forgotten. An audit log is read-only. The tether is bidirectional: production decisions stream to your desktop, your reviews stream back as new training signal, and the agent matures further. The relationship is the moat.
Can I roll back to an earlier version of an agent?
Yes. Every meaningful change is a versioned commit (agent definition + LoRA adapter + memory snapshot + Bayesian profile). Rollback restores all four atomically.
05İlgili sayfalar
yanındayım — Ilura