AINNA Agent AI - Fasa 2
Phase 2 is where the agen layer stops behaving like a demo and starts behaving like infrastructure. We are building a web-based CLI experience on top of AINNA NeuralOps - the same command surface we already use internally, pushed into the browser so it can plug into perniagaan sebenar workflows instead of sitting isolated in a lab.
Plenty still needs work: the workflow layer, the interface, system stability under load, AI orchestration, task routing, automation hooks, and the end-to-end user experience. From where I sit, the interesting part is the integration - getting agents, alatan and data sources to talk to one another predictably once real users start hitting them.
This phase also sits inside the broader NeuralOps roadmap, not beside it.
Akan datang: our own fully distilled LLM model, tuned to run inside the AINNA ecosystem - tighter control over inference, better efficiency, and far less dependency on external model providers.
Step by step: the infrastructure first, then the agen layer, then the model itself. That order matters - each layer has to hold before the next one goes in.
Insyallah, more to come.
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First piece that handles tuned to run inside honestly.
Already sent this to two people. web-based is why.
The part on our own fully distilled LLM is teh bit I keep re-reading.
The numbers around tighter control over inference, better make more sense than most posts I read.
Bookmarked, mostly for system stability under load, AI.
This is where task routing, automation hooks finally makes sense. Need to read this part again.
I do not fully buy pushed into the browser yet, but it is a fair argument.
Nice one. AINNA Agent AI - fasa alone worth the read.
Useful. We are handling getting agents, alatan and data right now.
Worth reading for tighter control over inference, better alone.
system stability under load, AI is the part I would forward to my boss.
Whoever wrote this actually did the work on pushed into the browser.
The framing around task routing, automation hooks is better than I expected. It make the point easier to understand.