Claims should be inspectable
Maturity, safety, and readiness language should point to code, tests, benchmarks, review records, or an explicit limitation.
THE PERSON BEHIND THE ATLAS
The Multi-Agent AI Atlas was created by Mahsa Keikha, PhD, as a public engineering reference for people designing AI systems that must coordinate, use evidence, interact with tools, and remain accountable to human decision owners.
Agentic AI is often presented through polished outputs. The difficult engineering work happens earlier: defining responsibilities, controlling tools, tracing evidence, managing state, handling disagreement, testing failure, and deciding which actions remain protected.
The Atlas brings those questions into one connected body of work. Its 170 systems show how the same engineering disciplines change across healthcare, robotics, software, research, finance, manufacturing, education, public systems, and other domains.
The work is grounded in a simple position: increasing machine capability should increase the quality of human oversight, not make responsibility harder to locate.
ENGINEERING COMMITMENTS
Maturity, safety, and readiness language should point to code, tests, benchmarks, review records, or an explicit limitation.
Protected actions, approval requirements, escalation, and prohibited authority belong in the architecture from the beginning.
A useful system exposes uncertainty, missing evidence, disagreement, tool failure, and the conditions that stop execution.
Corrections, reproducible challenges, security reports, and domain review improve the reference and its usefulness.
WORK WITH THE ATLAS
Architecture review, readiness assessment, evaluation design, governance, and governed prototype direction.
Explore partnershipExecutive briefings and decision support for responsible investment, adoption, risk, and ownership.
Explore trainingArchitecture comparison, teaching materials, reproducible critique, citation, and research collaboration.
Inspect evidence