The 6 AI Architecture Primitives
Most teams begin agentic AI design with a model, prompt, and orchestration framework. That can create impressive demos—but not necessarily systems that are safe, reliable, cost-controlled, or ready to act in the real world.
This session introduces six architecture primitives that every production AI agent needs:
Reasoning — turning an objective into bounded plans and decisions
Memory — retaining the right context without carrying unsafe or stale state
Tools — taking action through APIs, workflows, and enterprise systems
Evidence — grounding outputs in current, authorized, traceable facts
Policy — defining authority, permissions, constraints, and approval rules
Control — observing behavior, enforcing budgets, degrading safely, and stopping unsafe autonomy
Participants will see how these primitives work together as an AI agent moves from “answering a question” to making a real-world recommendation or action.
An agent that has memory but no policy can retain information it should not use. An agent with tools but no authority control can take actions beyond its business mandate. An agent with strong reasoning but weak evidence can produce a convincing wrong answer. An agent with all of these capabilities but no control layer can retry itself into cost spikes, duplicate side effects, or unsafe decisions.
The key challenge is not simply building more capable agents. It is governing autonomy: deciding what the AI may know, use, recommend, commit, defer, and stop doing when conditions become uncertain.
Session details:
Using an AI-commerce agent as a live architecture case, the session traces a request from customer question to possible business action. The agent must retrieve customer and policy context, evaluate evidence, call inventory and payment tools, and decide whether to answer, recommend, issue a refund, or escalate.
Then the system is pressure-tested through Authority and Degradation simulations:
- The agent retrieves a stale return policy but a current exception exists elsewhere.
- Inventory is delayed, but the agent wants to promise two-day delivery.
A payment tool times out after a possible charge. - The model has enough information to recommend an action—but lacks authority to commit it.
- Tool calls and retries exceed the agreed churn budget.
- A required source becomes unavailable or an entitlement check fails.
- The system is operationally healthy, but its evidence is no longer trustworthy.
For each scenario, participants determine:
- Which architecture primitive has failed or become insufficient?
- What is the agent still allowed to say or do?
- Which policy and evidence checks are required?
- Should the system remain normal, become conservative, defer, freeze a capability, or escalate to human review?
- What receipt must be recorded so the decision can later be replayed and defended?
Value for participants:
- A clear mental model for designing production-grade agentic AI beyond prompts and model selection.
- A way to separate AI reasoning from business authority and policy enforcement.
- Practical patterns for memory boundaries, tool permissions, evidence qualification, and safe retrieval.
- A method to prevent uncontrolled retries, tool loops, excessive cost, and duplicate actions.
- A degradation strategy that reduces autonomy safely without shutting down the full customer experience.
- Reusable questions to pressure-test an existing AI agent design.
Attendees will leave with:
- The Six AI Architecture Primitives reference model
- An Authority Boundary Map for agent capabilities
- An Evidence and Policy Admission Checklist
- A Tool and Churn Budget template
- An Authority and Degradation Scenario exercise
- A Stop Authority Ladder: Normal → Conservative → Defer → Freeze → Human Review
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.