Sold by: Mphasis
Deployed on AWS
Accelerates the deployment of efficient AI agents by transforming static documentation into synthetic training data to bake complex tool-reasoning and SOP compliance directly into specialized Small Language Models.
Overview
This solution is an end-to-end AI pipeline that automates the creation, training, and evaluation of specialized Small Language Models (SLMs). It first extracts business rules from uploaded SOPs and processes user-provided tool descriptions for the backend agent, integrating real customer data to build grounded, realistic scenarios. Using these constraints, a synthetic user agent converses with the backend agent to achieve specific scenario objectives, generating a high-fidelity dataset of complex tool-use interactions and edge cases.
This rich data is used to train a lightweight SLM. Through this distillation process, custom behaviors and SOPs that previously required bulky text context for the backend LLM are now fully internalized by the small LM. Finally, an LLM-as-a-Judge evaluates post-training interactions, rigorously verifying tool retrieval accuracy, tool use accuracy, correctness, and faithfulness to ensure the model is production-ready.
Highlights
- AgentSLM automates a hands-off lifecycle that transforms static SOPs and tool descriptions into an optimized Small Language Model (SLM). By distilling complex business logic and tool-reasoning behaviors directly into the model's weights, it eliminates the need for manual dataset creation and removes the dependency on bulky, context-heavy prompts in production. This creates a lightweight, high-performance agent that has your specific operational "playbook" natively internalized.
- The solution intelligently extracts granular rules and variables to construct realistic, grounded scenarios. A synthetic user agent is then deployed to engage the backend agent, driven by specific objectives to uncover complex tool-use behaviors and combinatoric edge cases. This process is capped by a specialized LLM-as-a-Judge framework that rigorously evaluates tool retrieval accuracy, tool use, and faithfulness, ensuring the distilled model is both highly capable and production-ready.
Details
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Delivery method
Supported services
Delivery option
Deploy AgentSLM on Amazon ECS (Fargate) using CloudFormation
Latest version
Operating system
Linux
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Per Pipeline Run | Billed each time an SOP document is processed and the generative AI pipeline executes. | $3.00 |
Vendor refund policy
NIL
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Delivery details
Deploy AgentSLM on Amazon ECS (Fargate) using CloudFormation
Supported services: Learn more
- Amazon ECS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
This is the first version.
Additional details
Usage instructions
Support
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