Big starts
early.
EarlyBig is an AI research and systems company working on the foundations required for capable, reliable and increasingly autonomous machine intelligence.
We are interested in the gap between models that look capable in isolation and systems that remain capable under real constraints.
Reasoning & agency
Planning, tool use, long-horizon execution, delegation, self-correction and coordination across multiple agents. We care about what makes these systems robust, not merely impressive in short demonstrations.
Evaluation
Methods for measuring behavior that matters in deployment: reliability, controllability, failure recovery, calibration, adversarial robustness and performance under changing context.
Memory & context
Representations and architectures for persistent knowledge, retrieval, state, episodic memory and context construction across extended interactions and large heterogeneous information spaces.
Control & governance
Technical mechanisms for constraining what advanced systems may do: authority, policy, budgets, review, auditability and runtime control. Governance should be an executable property of the system, not a document beside it.
AI systems
Inference, orchestration, data movement, model access, observability and distributed execution. Model quality matters; so does everything required to make useful intelligence economical and dependable at runtime.
Research that
becomes systems.
The platform is not a wrapper around a single model provider. The goal is to provide a rigorous systems layer on which increasingly capable AI can be built, measured and operated.
Runtime
Execution primitives for model calls, tools, agent state, workflows and long-running processes.
Control plane
Identity, authority, policy, budgets, approvals and runtime constraints for consequential actions.
Evaluation layer
Continuous evaluation of models, agents and system behavior across offline tests and live execution.
Observability
Structured traces of reasoning, tool calls, state transitions, policy decisions, cost and failure modes.
Model & compute abstraction
A systems interface across model providers, local models and specialized inference backends without forcing the application to collapse onto one stack.
Why agent reliability is a systems problem
Model capability is only one component of long-horizon behavior. State, environment, tool contracts, error propagation and control mechanisms increasingly dominate what happens in production.
Beyond benchmark accuracy
Useful evaluation needs to expose failure structure: where systems degrade, what they recover from, what changes under distribution shift, and what remains stable across repeated execution.
Governance as an execution primitive
As AI systems gain agency, policy needs to move into the runtime path. Permission, authority and review become part of the architecture of intelligence itself.
Work on hard
problems.
We are interested in conversations with researchers, engineers and teams working on technically difficult problems in advanced AI.
hello@earlybig.com