The Commitment Engine: A Practical Control Plane for Enterprise AI
Enterprise AI is moving beyond answering questions. Agents now retrieve data, call tools, recommend actions, modify workflows, and influence customer outcomes.
That changes the architecture problem.
An enterprise AI system is not merely a workflow engine. It is a commitment engine: a system that must know what it is allowed to attempt, what it is allowed to claim, when it crosses a liability boundary, and how it can prove its decision later.
In this practical architecture session, Rohit Bhardwaj introduces a control-plane model for governing AI agents at scale. Participants will learn how to connect authority, policy, evidence, freshness, cost, human approval, and operational controls into one coherent design.
Using a realistic AI-commerce-agent scenario, the session follows an agent that wants to answer a customer, retrieve policy, inspect payment and inventory status, and issue a refund. Then reality changes: evidence becomes stale, policies conflict, a payment is ambiguous, retries rise, and the agent’s tool-call budget is nearly exhausted.
Rather than asking only, “Is the service up?”, we ask the more important question: Is the system still allowed to act?
Attendees will use practical artifacts from the System Design with AI Interview Guide companion lab: the Commitment Engine Answer Template, Liability Boundary Map, Admissibility Contract, Freshness Clocks Template, Churn Budget Template, ROCS Loop, Stop Authority Template, and Decision Replay Card.
The outcome is a repeatable method for designing enterprise AI systems that can act, defer, stop, explain, and earn trust.
Learning objectives
Participants will be able to:
- Identify the commitment boundary—the moment a system stops attempting and starts owing.
- Define the authority, policy, evidence, and freshness requirements needed before an agent may act.
- Apply churn budgets to bound retries, tool calls, fan-out, latency, and inference cost.
- Design a capability-scoped stop-authority ladder: Normal, Conservative, Defer, Freeze, Human Review, and Re-enable.
- Create a Decision Replay Card that proves why the system acted, what it knew, and what could reverse the outcome.
- Use ROCS—Reliability, Observability, Cost, and Sustainability—as one operating control loop rather than four disconnected dashboards.
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.