Evidence-Bound
Every claim is tied to verifiable evidence with explicit confidence and uncertainty.
Forensic AI for Agentic Systems
Akiwaki reconstructs and validates the causal pathways behind autonomous AI agent actions.
We move beyond logs and observability to deliver evidence-bound explanations and counterfactual validation.
Causal Reconstruction
We reconstruct activated pathways and identify what truly caused the outcome.
Counterfactual Validation
We test candidate causes through counterfactual replay and measure outcome change.
Unknown is a Valid Outcome
When evidence is missing or insufficient, we report unknown, not speculation.
The Problem
Logs Don’t Explain Why
AI agents operate through complex cognitive, tool, identity, and environmental pathways. Logs show what happened, but not why it happened. And AI systems explaining themselves is not reliable for forensic purposes.
Our Approach
From Trace to Truth
Akiwaki applies a physiology-aware forensic framework to move from raw traces to validated causal explanations.
Read Our Research
Download the white papers to learn more about our framework, methodology, and roadmap.
Research-Led Forensics
Built for rigorous incident analysis across agents, tools, memory, identity, and environment.
Evidence-First Materials
White papers, diagrams, methodology notes, and benchmark plans can be linked here.