How can AI agents operate within platforms that are extensible, resilient, and governed across multiple tenants?

AI Agent Platform Engineering: Designing Multi-Tenant, Extensible Agent Systems examines the architectural and computational foundations of shared agent infrastructure. Nachiket R Joshi and Maulik Bhatt explore modular platform design, security and governance mechanisms, distributed state reconciliation, and resource contention.

The book develops topics including federated rotating-leader consensus, stochastic Markov forecasting, continuous probabilistic tool optimization, formally verified synthesis, and self-certifying neural containment. Its chapters connect system models and mathematical formulations with experimental evaluations, trade-offs, and implementation considerations.

Readers encounter approaches to checkpointing and recovery, tenant isolation, fair resource allocation, generated-code verification, agent monitoring, and trusted execution environments. Together, these topics provide a technical perspective on coordinating autonomous agents while managing reliability, computational constraints, and operational risk.

For researchers, platform architects, and engineers studying the design of multi-tenant AI agent systems.