Bikkamora Labs
Deep-dive engineering tracks built around realistic production systems, deterministic control invariants, and failure postmortems.
Agentic AI
A structured engineering curriculum covering the transition from simple prompt-response wrappers to deterministic execution loops, schema validation barriers, bounded recursion, and offline trajectory evaluations.
- ✓ Design deterministic Directed Acyclic Graph (DAG) state machines for multi-step agent workflows
- ✓ Enforce strict JSON Schema validation boundaries on LLM tool invocations with additionalProperties: false
- ✓ Implement circuit breaker governors to prevent runaway recursion and token-spend depletion
- ✓ Build replayable offline evaluation harnesses to detect non-deterministic agent decision degradation
Data Engineering
High-throughput ingestion, physical storage engines, and transaction isolation
Upcoming track focusing on physical page layouts, LSM-Trees vs B-Trees, Write Amplification Factors, and transactional anomalies under high write load.
AI Engineering
Inference runtime optimization, vector index mechanics, and model evaluation
Upcoming track on building production AI infrastructure: KV-cache management, quantization trade-offs, and embedding retrieval mechanics.