Version 1.0
ACAAI is an open framework for engineering accountability into agentic AI systems throughout their lifecycle.
As AI systems become increasingly autonomous, accountability can no longer rely solely on governance processes or pre-deployment controls. ACAAI approaches accountability as a systems engineering property that emerges from the coordinated implementation of organizational and technical controls across the broader socio-technical system.
ACAAI organizes accountability into six complementary domains each containing different controls:
- Organizational Governance
- Human Oversight, Consent & Decision Authority
- Identity, Authority & Data Governance
- Observability, Explainability & Evidence
- Runtime Safety & Assurance
- Incident Response & Recovery
Together, the six domains provide a layered accountability architecture in which organizational governance, operational processes and engineering controls reinforce one another.
ACAAI includes 100+ organizational and technical controls for operationalizing accountability across the agentic AI lifecycle. The controls are intended to support a risk-based implementation.
The complete ACAAI Version 1.0 framework is available as a PDF in this GitHub repository and in Zenodo:
ACAAI does not propose an entirely new set of principles. Instead, it brings together existing knowledge from AI governance, cybersecurity, systems engineering, safety engineering, resilience engineering and AI assurance and translates it into a coherent framework for engineering accountability into autonomous AI systems. The framework is intended to complement existing practices rather than replace them.
ACAAI is an open and evolving framework and it is not presented as a definitive solution.
ACAAI is intended as a foundational engineering framework rather than a prescriptive implementation methodology, recognizing that many of the proposed controls remain at an early stage of maturity, requiring further research before they can be consistently implemented in practice.
Further research and practical implementation are encouraged to evaluate the framework, validate the effectiveness of its controls, identify limitations and gaps, and develop new approaches as agentic AI systems and their associated risks continue to evolve.
If you use or reference ACAAI in research, publications or professional work, please cite the framework:
Barberá, I. (2026). ACAAI - Accountability by Design for Agentic AI: A Lifecycle Framework for Engineering Accountability in Autonomous AI Systems (Version 1.0). Zenodo. [https://doi.org/10.5281/zenodo.21856005]
Feedback, research, implementation experience and proposals for improving ACAAI are welcome.
Please use GitHub Issues to report errors, propose changes, discuss controls or identify areas requiring further research.
ACAAI is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to share and adapt this work, including for commercial purposes, provided that appropriate attribution is given and derivative works are distributed under the same license.
Please note that the license applicable to the framework text and figures may differ from licenses commonly used for software source code.
Isabel Barberá
ORCID iD: 0009-0006-7508-5629
‘Accountability means taking responsibility for decisions, actions and their outcomes. In autonomous AI systems, accountability cannot rely solely on organizational governance or individual technical controls. It must be embedded into the design, governance and operation of the system throughout its lifecycle. Accountability becomes therefore an emergent system property arising from the interaction of organizational governance, human oversight, delegated authority, runtime controls, observability and incident response. Only the coordinated operation of these controls can ensure accountability across the AI lifecycle.’
— ACAAI Foundational Principle