The Science Behind Nika
We're not just building another CLI tool. We're researching new approaches to make AI workflows safer, more predictable, and genuinely useful for developers.
Note: This is ongoing research. Not everything described here will make it into v1.0. We're sharing our thinking openly to gather feedback and find collaborators.
Epistemic Awareness
Making AI Know What It Knows
A framework for AI systems to understand their own knowledge boundaries, detect hallucination risks, and signal uncertainty in real-time.
Antifragile Workflows
Systems That Get Stronger From Stress
Inspired by Nassim Taleb's work, we're exploring how AI workflows can become more robust through exposure to failures, not despite them.
Scope Isolation
The 3D Context Architecture
A novel approach to managing AI agent context across three dimensions: DAG position, transcript history, and state exposure.
The SHAKA System
Smart Hybrid Advisory Kernel Agent
A runtime sidecar that observes, analyzes, and proposes optimizations without ever executing. "SHAKA proposes. NIKA disposes."
DAG Execution Engine
Deterministic Workflow Orchestration
Directed Acyclic Graphs for predictable, parallel AI workflow execution. Maximum control with automatic parallelization.
Context Engineering
Token Optimization for AI Agents
Advanced context window management: observation masking, trajectory compression, and smart token allocation based on latest research.
Multi-Provider Orchestration
One Workflow, Any LLM
Seamless switching between Claude, GPT-4, Gemini, and local models. No vendor lock-in, intelligent routing, cost optimization.
Bounded Rationality
AI That Knows Its Limits
Herbert Simon's satisficing principle applied to AI agents. Find good-enough solutions fast, rather than exhaustively searching for optimal ones.
Graceful Degradation
Workflows That Bend, Never Break
When components fail, maintain core functionality. Model fallbacks, provider switching, and automatic recovery paths.
Declarative Intent
Describe What, Not How
YAML-first philosophy: declare your desired outcome, let the engine handle execution. 60% less code than imperative frameworks.
Chaos Engineering
Break Things on Purpose
Netflix-inspired resilience testing for AI workflows. Inject failures, test fallbacks, validate recovery before production breaks.
Self-Healing Agents
Autonomous Error Recovery
AI workflows that detect failures and fix themselves. Runtime signals trigger automatic healing actions without human intervention.
Observability-Driven Development
If You Can't Observe It, You Can't Debug It
Traces, spans, metrics, and structured logging for every agent. OpenTelemetry-compatible. Debug non-deterministic AI like never before.
Interested in our research? Want to contribute?