Research that ships.
Our research program exists to solve hard problems in enterprise AI — and to feed those solutions directly into production systems. This is not academic publishing for its own sake. Every research question we pursue has a path to the platform.
Why research matters now.
The gap between what AI models can do and what organizations can safely deploy is widening. Models get more capable every quarter. The systems to govern, ground, and operate those models are barely being built.
StratafAI invests in research because the hardest problems in enterprise AI are not model problems. They are systems problems, organizational problems, and governance problems. These require rigorous thinking, not just engineering.
Every research area directly informs our platform capabilities. Org-Graph theory drives our context layer. Agent control systems research drives our governance tools. Context engineering research drives how we manage the scarcest resource in AI.
Org-Graph Theory
Organizational structure as machine-readable context. How do you represent the complexity of a real organization in a form that AI agents can consume, reason about, and act within?
Organizational Modeling
Formal representations of roles, teams, reporting structures, and cross-functional relationships as computationally useful graphs.
Decision Rights & Authority
Modeling who can decide what, budget thresholds, approval chains, and escalation triggers as traversable structures.
Incentive & Accountability Mapping
How measurement, incentives, and accountability structures shape behavior — and how agents should account for them.
Dynamic Organizational Context
Organizations change. Research into how Org-Graph representations stay current as teams shift, roles evolve, and processes adapt.
Agent Control Systems
How do you govern AI agents that reason, act, and operate autonomously? The control problem is not theoretical — it is an engineering problem that organizations face today.
Runtime Governance
Real-time policy enforcement during agent execution. How to constrain without crippling. How to govern without creating bottlenecks.
Policy Engines
Declarative policy languages for agent behavior. Defining what agents can do, when, and under what conditions.
Drift Detection
Identifying when agent behavior deviates from expected baselines. Silent degradation is the primary risk of production agent systems.
Cost/Benefit Optimization
Balancing agent capability against resource consumption. Token economics, latency trade-offs, and value-per-interaction analysis.
Context Engineering
Context windows are the scarcest resource in AI. How you compose, prioritize, and manage context determines the quality of everything an agent does.
Context as Scarce Resource
Token limits create hard constraints. Research into optimal allocation, priority ranking, and dynamic context budgeting.
Context Composition
Assembling the right context from multiple sources — organizational data, task state, user history, domain knowledge — in the right proportions.
Organizational vs. Task Context
Balancing stable organizational context with dynamic task-specific context. How to ground agents without consuming their entire window.
Atlas Experiments
Atlas is not just our platform UI — it is our primary experimental environment. We build, test, and iterate on ideas here before they become production features.
Prototypes
Experimental agent interfaces, visualization approaches, and interaction patterns. Not everything ships. Everything teaches.
Internal Tools
Tools we build for ourselves that often become tools for customers. Dogfooding as a research methodology.
Open Questions
The problems we haven't solved yet. Published for transparency and to invite collaboration from the community.
Interested in our research?
We publish findings, share prototypes, and engage with collaborators. Get in touch if our research areas overlap with your challenges.