Engineering the Next Architecture for Human-Agent Collaboration
Keywords:
Agentic AI, Information Architecture, Human-AI Interface, Systems Engineering, Developer Experience, AI WorkflowsAbstract
This paper examines how an engineering-led approach to information architecture, sprint planning, and quick-start flows can materially improve developer productivity and reliability outcomes. In the initial state, a fragmented and weakly structured knowledge base required AI developers to infer platform behavior from tribal knowledge, ad hoc Slack threads, and outdated implementation guides, increasing onboarding time and error rates. This paper describes a re-design in which documentation is treated as a system with an explicit architecture—comprising Quick Start, Introduction, Environment Setup, First Use Case, Core Capabilities, Observability, and Special Topics—mirroring established systems-engineering practices for modularization and interface definition. By applying classic systems engineering principles to knowledge flows, the team transformed documentation from a passive artifact into an actively engineered component of the developer platform, yielding more predictable first-success paths, reduced support load, and a foundation for future automation and AI-assisted guidance.
References
1. Chen, M. et al., 2023. Agentic AI: Architectures and Challenges. Journal of Artificial Intelligence Research, 72, pp.123-145.
2. Wang, X. & Li, J., 2022. Multi-agent Systems for Complex Task Coordination. IEEE Transactions on Neural Networks and Learning Systems, 33(4), pp.1472-1485.
3. Zhang, Y. et al., 2021. Recursive Reasoning in AI Agents: ReAct and Beyond. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), pp.6543-6550.
4. Smith, A. & Patel, R., 2020. Memory Architectures in Intelligent Agents. Cognitive Systems Research, 61, pp.45-56.
5. Brown, T. et al., 2020. Language Models Are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, pp.1877-1901.
6. Russell, S. & Norvig, P., 2016. Artificial Intelligence: A Modern Approach. 3rd ed. Pearson.
7. Mnih, V. et al., 2015. Human-Level Control Through Deep Reinforcement Learning. Nature, 518(7540), pp.529-533.
8. LeCun, Y., Bengio, Y. & Hinton, G., 2015. Deep Learning. Nature, 521(7553), pp.436-444.
9. Sutton, R.S. & Barto, A.G., 2018. Reinforcement Learning: An Introduction. 2nd ed. MIT Press.
10. Doshi-Velez, F. & Kim, B., 2017. Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
11. Chollet, F., 2019. On the Measure of Intelligence. arXiv preprint arXiv:1911.01547.
12. Marcus, G., 2020. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv preprint arXiv:2002.06177.
13. Chen, D. et al., 2022. Chain of Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903.
14. Janner, M. et al., 2021. Offline Reinforcement Learning with Implicit Q-Learning. Advances in Neural Information Processing Systems, 34, pp.1116-1127.
15. Vinyals, O. et al., 2019. Grandmaster Level in StarCraft II using Multi-Agent Reinforcement Learning. Nature, 575, pp.350-354.
16. Zilberstein, S., 2015. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. Morgan & Claypool Publishers.
17. Lipton, Z.C., 2016. The Mythos of Model Interpretability. arXiv preprint arXiv:1606.03490.
18. Levesque, H.J., Davis, E. & Morgenstern, L., 2012. The Winograd Schema Challenge. Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning, pp.552-561.
19. Goodfellow, I., Bengio, Y. & Courville, A., 2016. Deep Learning. MIT Press.
20. Bommasani, R. et al., 2021. On the Opportunities and Risks of Foundation Models. arXiv preprint arXiv:2108.07258.
21. Sutton, R.S., Precup, D. & Singh, S., 1999. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence, 112(1-2), pp.181-211.
22. Pearl, J., 2009. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press.
23. Amershi, S. et al., 2019. Guidelines for Human-AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp.1-13.
24. Sutton, R.S., 1998. Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks, 9(5), pp.1054-1054.
25. Kwiatkowska, M., Norman, G. & Parker, D., 2011. PRISM 4.0: Verification of Probabilistic Real-time Systems. Computer Aided Verification, pp.585-591.
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