SnapLogic today announced new capabilities across its Agentic Integration Platform that make it easier for enterprises to build AI agents, connect them to business systems and data, and put them to work across complex processes. Spanning SnapGPT, SnapLogic MCP Server, and AgentCreator, the latest enhancements help enterprise teams move agentic AI from development into production while reducing the integration work required to put AI to work across the business.

SnapLogic’s latest capabilities give enterprises a more direct path from AI experimentation to production. New capabilities include:

  • Building complete agent workflows with natural language: SnapGPT can now generate complete agent and MCP Server pipelines directly from natural-language prompts, reducing the manual work required to assemble agentic workflows. Additional enhancements include persistent chat history, refreshed skill descriptions, and the ability to save AI-generated pipeline descriptions directly to the pipeline.
  • Connecting AI agents to 44 enterprise technologies: SnapLogic introduced 545 prebuilt agentic-tool pipeline patterns that expose third-party enterprise services as standardized MCP servers. The ready-to-use patterns allow agents to retrieve data, take actions, and automate tasks across enterprise systems without requiring teams to build each integration from scratch.
  • Giving agents the tools to operate enterprise workflows: SnapLogic MCP Server now includes 15 new tools that allow AI agents to monitor pipeline runs, diagnose failures, check environment health, manage users and groups, and review scheduled tasks without leaving the AI-assisted development session. Additional capabilities support large file uploads and execution controls, while bulk MCP log downloads give teams greater visibility for auditing and troubleshooting.
  • Building production-ready RAG with Amazon Bedrock: Three new Amazon Bedrock Snaps — MKB Retrieve, MKB Agentic Retrieve, and MKB Ingest Documents — connect SnapLogic pipelines directly to an Amazon Bedrock Managed Knowledge Base. Enterprises can ingest and query their data directly from a pipeline without custom retrieval or ingestion code, supporting use cases such as support chatbots grounded in enterprise documentation, document-based Q&A, and scheduled knowledge base refreshes.

To learn more about the SnapLogic Agentic Integration Platform, visit: https://www.snaplogic.com/.

Flexera Expands AI Cost Management Platform and Broadens Ecosystem to Help Customers Control AI Spend while Optimizing Business Value
Flexera announced the expansion of its AI Cost Management platform with the addition of cost controls for AI cloud infrastructure and AI data cloud platforms, building on its current cost management offerings that span AI applications, agents, models, and workflows. The company also announced the expansion of its AI ecosystem including gateways, observability, routing, and economic control systems, as well as the availability of a new AI cost management maturity assessment.

 

AI Cost Management Platform Expansion

Flexera is expanding its AI Cost Management solution with new autonomous capabilities in its Data Cloud Optimization and Workload Optimization offerings. With autonomous agents continuously balancing cost efficiency and performance, engineers can focus on innovation. Flexera helps maintain cost-efficiency and reliability in dynamic AI environments across clouds, containers, and leading data platforms such as Databricks and Snowflake. Snowflake automation is scheduled to be released by the end of this year, with Databricks automation to follow in early 2027.

These additions join Flexera’s Token Cost Control as part of its full AI Cost Management solution. Token Cost Control gives organizations visibility and accountability for AI spend across leading AI model APIs and SaaS applications, including which users and agents are driving it. Since its first availability earlier this year, Token Cost Control has enabled organizations to:

  • See usage and cost for AI SaaS applications and model APIs
  • Identify users and agents who are driving AI spend
  • Allocate or charge back AI costs to those responsible for it
  • Analyze trusted data for AI cost governance and strategic decision-making

“AI is one of the fastest-growing areas of technology spend, with worldwide investment forecast estimated to rise 47% this year. Scaling AI responsibly will require far greater visibility, accountability, and financial discipline,” said Mike Jerich, President and CEO of Flexera. “Flexera helps organizations understand, govern and optimize spending across the full AI stack, from model consumption and AI-powered applications to the infrastructure and data platforms that power AI at scale, connecting growing costs to measurable business value.”