The global race to develop production-ready AI agents is intensifying—and Amazon Web Services (AWS) is making strategic moves to reduce complexity and accelerate deployment. At AWS re:Invent 2025, the company announced a suite of new capabilities designed to help organisations transition from proof-of-concept prototypes to scalable, real-world AI systems.
Many businesses struggle to operationalise agents due to high infrastructure costs, specialised machine learning expertise, and slow training cycles. AWS’ newly launched features—Reinforcement Fine Tuning, Amazon Bedrock AgentCore Policy & Evaluations, and AgentCore Memory with episodic learning—aim to solve these bottlenecks by automating training, establishing clear behavioural controls, and improving contextual reasoning.
AWS claims that early adopters have already experienced dramatic improvements. According to internal benchmarks, Reinforcement Fine Tuning (RFT) can deliver up to 66% accuracy gains compared to base models, while Collinear AI reports reducing experimentation time from weeks to days with the latest SageMaker enhancements.
“Most companies use the largest models for every task, but much of agent activity involves routine actions like calendar checks or document search,” says Dr. Swami Sivasubramanian, Vice President of Agentic AI at AWS. “This leads to slow responses and unnecessary spending. We aim to make agents faster, more efficient, and more cost-effective.”
🔗 More AI updates:
https://www.tlogies.net/search/label/AI%20News
Reinforcement Fine Tuning: Turning Generic Models into Specialists
RFT enables developers to customise foundation models without needing deep AI expertise or infrastructure management. Users simply choose a model, upload datasets or invocation logs, define reward rules, and AWS automates the rest using a serverless pipeline.
At launch, the feature supports Amazon Nova 2 Lite, with additional models planned.
Phil Mui, SVP of Software Engineering for Agentforce at Salesforce, says RFT performance improvements reach up to 73% in accuracy, enabling more personalised enterprise AI systems. Fine-tuning prioritises data quality: a curated dataset of 10,000 meaningful agent interactions can outperform millions of generic training samples.
Swami compares the approach to medical specialisation:
“Fine-tuning is like transforming a general doctor into a cardiologist—highly focused and highly effective.”
Amazon Bedrock AgentCore: Policy Controls and Performance Evaluations
To reinforce safety and governance, AWS introduced AgentCore Policy, enabling organisations to define behavioural rules in natural language. Policies can control which tools or external services agents may access and establish conditional restrictions.
For example:
“Block all refunds over $1,000 without manager approval”
prevents agents from executing high-value actions automatically.
AgentCore Evaluations includes 13 built-in evaluators that monitor metrics such as:
-
Response correctness
-
Helpfulness and goal success rate
-
Tool usage accuracy
-
Safety and compliance
-
Context relevance
The system reviews live interactions and triggers alerts when performance dips—such as notifying supervisors if satisfaction scores drop 10% within a specified period.
AgentCore Memory: Episodic Experience for Long-Term Intelligence
One of the most transformative updates is the episodic memory layer, enabling agents to learn from previous interactions instead of starting from zero on each request. Episodes store context, reasoning paths, actions, and outcomes that can be reused for future decisions.
Swami compares the experience to personalised service:
“Like the staff at your favourite restaurant remembering your name and preferred dish—effective agents need both short-term and persistent long-term memory.”
S&P Global Market Intelligence has deployed the capability across a distributed agent platform named Astra. The company previously struggled to maintain consistent state across hundreds of specialised agents, but the new unified memory layer supports scalable orchestration.
Helene Astier, Head of Technology & Sustainability at S&P Global MI, said the upgrade was essential:
“Managing agent context at scale became extremely challenging. Episodic memory provides a stable framework to coordinate and optimise distributed agent workflows.”
Conclusion
With Reinforcement Fine Tuning, AgentCore policy controls, evaluation automation and memory-based reasoning, AWS is positioning itself to lead the next phase of agentic AI—where reliability, safety, and efficiency matter as much as raw model power.
As AI systems move from experiments to enterprise-wide deployment, these innovations could fundamentally accelerate adoption across customer service, automation, analytics and digital workforce ecosystems.
🔗 Read more AI developments:
https://www.tlogies.net/search/label/Tech%20News
