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At this year’s AWS re:Invent, Dr. Swami Sivasubramanian, VP of Data and AI at AWS, began his keynote by reflecting on the first program he ever built. In high school, he only had a basic calculator — you know, the kind that can only add, subtract, multiply, and divide — and on a shared school computer he ended up programming his own scientific calculator. Swami described the exhilarating feeling of having the freedom to imagine and create. That same feeling, he said, is now being unlocked by the shift toward agentic AI, which lets people open up a “whole new world of possibilities.”
He used that setup to introduce a broader argument that the industry is moving beyond chatbots into agentic systems that can reason, act, and learn as they operate in real enterprise environments. Swami framed this not as a speculative or conceptual future but as a real architectural move already underway — an era in which organizations build and operate software differently. This is defining the next era of development and will undoubtedly lean on AI agents that can interpret objectives and act independently.
Agentic AI is a game changer, Swami explained, because it shifts who can build and how quickly they’re able to do so. Rather than battling APIs or frameworks, developers can tell the system what they want in plain language and let the system figure out how to get there. The pace of that process has accelerated as well, with work that used to require development cycles is now being turned around in a matter of days. Agents effectively take intent as input and turn it into working actions.
Natural language, he said, breaks down barriers for developers, and the speed gains change who can build, how they build, and how quickly ideas move from concept to production.
To give a sense of how different agentic AI is from chatbots, he cited an example where traffic to a site falls off a cliff by 40%. A chatbot may recommend looking at analytics or checking recent updates. An agent would investigate further and analyze the data, look through logs, troubleshoot, and create a ticket with an actual solution. That’s the fundamental difference: chatbots offer a direction; agents take more responsibility for the outcome.
According to Swami, an agent is composed of three basic parts: the model responsible for reasoning, the code describing its purpose, and the tools used to interact with real systems. Previous approaches often relied on stiff orchestration that glued these parts together, rendering agents inflexible and difficult to apply in new situations.
This is where the Strands Agents SDK from AWS enters the picture. Instead of writing pages of orchestration code, with Strands, users can put that logic into the model itself. This means developers can avoid scaffolding and concentrate on their actual agent. After AWS open-sourced it, the community began to adopt it quickly. Swami said they’re currently working on adding TypeScript support and enabling agents to run on edge hardware.
This brought him to the problem he called the “production gap.” Many agent demos work fine on a stage, but running them at scale often results in failure. They are not designed to accommodate spikes in sessions. They don’t enforce identity or access rules. They definitely don’t provide the visibility engineers want when something breaks. Swami’s point was that these missing puzzle pieces are why promising prototypes never make it to real systems — and that is why AWS is now building a runtime for the work that actually happens in production.
The keynote’s tone shifted gears when Swami reached Bedrock AgentCore. Until that moment, the keynote had been about what agents could do, but now Swami turned to what it takes to make those agents dependable.
Many companies can make a demo, but very few can operate agents that survive real traffic, permissions, and bugs. AgentCore is basically AWS drawing a line and saying: this is the production layer.
AgentCore is compatible with any model or framework and tackles the unglamorous pieces of infrastructure that teams would usually have to bolt together themselves — identity, access, connectivity, and the visibility engineers need to understand agent behavior.
(innni/Shutterstock)
Two major enhancements were also announced by Swami. The AgentCore Policy enables teams to write guardrails in natural language yet still have them enforced by formal verification. The reverse, AgentCore Evaluations, puts agents into large simulated environments and watches how they behave in order to learn what needs changing before they live. According to Swami, the stack is already being used by AWS clients for internal rollouts.
That builds the foundation for Episodic Memory. Short-term memory tracks a task. Long-term memory stores general information. But neither tells the agent why the present feels different. Episodic Memory can pick up patterns and adapt naturally.
Some preliminary results were also shared by Swami. Heroku just spent five weeks to build a fully agentic app-builder. PGA TOUR’s multi-agent pipeline delivered content at an unprecedented pace. And Caylent cut thousands of lines of orchestration code while using AgentCore.
Swami also covered the model layer: Reinforcement Fine-Tuning for Bedrock (up to 66% accuracy gains), using SageMaker’s serverless fine-tuning via natural language, and Nova Forge for domain-specific frontier models. You add Checkpointless Training for seamless recovery and Nova Act for reliable UI agents, and you can see how AWS is positioning to deliver a full stack of enterprise-level agentic systems.
Swami ended his keynote by referring back to where he began. The freedom to think and create. Agentic AI, said Swami, is designed to give that same freedom to anyone creating the next generation of AI systems.
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The post Swami’s AWS re:Invent Keynote Lays Out a Full-Stack Vision for Agentic AI appeared first on BigDATAwire.
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