Aqib Khan

Every top-layer patch, Vector DBs, RAG, custom wrappers, tries to force a probabilistic model to act like a traditional API. Mathematically, it fails because:
Probability is non-deterministic by definition.
Models continuously drift as weights update and context accumulates.
Instead of pretending we can turn probabilistic models into predictable binaries, production AI needs to focus on Constrained Reliability through Auditability.
You need:
Causal Lineage: Querying why an agent took a specific branch (walking execution trees, not just reading raw logs).
State Snapshots: Isolating exact context windows to debug drift.
Human-in-the-Loop Interceptions: Triggering governance before rogue actions hit production.

Most AI agent frameworks start with a while loop:

think call tool observe result repeat

That works, until you need the things production systems actually require:

Hey Product Hunt! I'm Aqib.

If there's one thing I've done consistently as a builder, it's fail.

I've launched products nobody used, spent weeks building features only to delete them, and chased ideas that seemed brilliant until reality proved otherwise. Every failure taught me something that eventually shaped how I build today.

By day, I work on AI platform and infrastructure engineering building the systems, orchestration, and harnesses that let AI models and agents run reliably at scale. I've found that the hard part isn't getting an LLM to answer a prompt it's building the infrastructure around it.