LangSmith BYOC on AWS is generally available
LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.
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The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record
arXiv:2611.06989v1 Announce Type: new Abstract: Enterprises run business transactions through multi-agent AI systems spanning clouds, large language models (LLMs), orchestration harnesses, and systems of record. Finance receives AI cost only at settlement grain, aggregated by SKU and charge period. The business question (what did this order, claim, or case cost in AI?) cannot be answered from billing data, because providers never see enterprise transaction boundaries. The FinOps Open Cost and Usage Specification (FOCUS) gives billing data one schema across providers but remains settlement-grain even as it adds AI token and requester attribution. We formalize this grain mismatch, derive seven requirements, and propose the Two-Ledger Model: a settlement ledger of FOCUS-conformant billing data joined to an attribution ledger of invocation-grain usage records built on OpenTelemetry GenAI telemetry. A layered correlation spine (trace context, A2A/MCP metadata, business-key correlation, probabilistic matching) assigns each record to its business transaction with recorded method and confidence; a versioned rate card prices usage in near real time; period reconciliation ties both ledgers to the invoice, and the unexplained residual identifies shadow AI. Costs are reported per order, claim, or outcome with agentic cost metrics. A simulation with known ground truth shows the spine sustaining cost coverage above 99% as trace propagation falls to zero, the reconciliation residual recovering planted unmetered spend within 0.24 percentage points while flagging provider billing errors, and mid-flight gating cutting p99 transaction cost by up to 61% at a 2-4% false-pause rate. We propose the attribution ledger as an invocation-grain FOCUS profile for standardization.
v1.19.0 - Trace Your Workflows, Layer by Layer
Dify 1.19.0 enhances observability with nested workflow tracing, allowing users to follow execution steps inside Workflow Tools, inspect LLM calls, and maintain context across nested invocations. The release also enables portable app packages via App packages, improves Agent file uploads and previews, introduces Skill Hub automation through CLI commands, and upgrades the bundled plugin daemon to version 0.6.14-local. Key changes include support for format v2 app packages, enhanced security, improved editing workflows, and updated environment variables and API endpoints.
n8n@2.46.3
## [2.46.3](https://github.com/n8n-io/n8n/compare/n8n@2.46.2...n8n@2.46.3) (2026-10-30) ### Bug Fixes * **core:** Add a cooldown before resending migration owner reminders ([#42567](https://github.com/n8n-io/n8n/issues/42567)) ([724efc3](https://github.com/n8n-io/n8n/commit/724efc352f51a52910924789b79cf66770807e09)) * **core:** Resolve IMAP trigger expressions after activation ([#42612](https://github.com/n8n-io/n8n/issues/42612)) ([17594a7](https://github.com/n8n-io/n8n/commit/17594a7eefa42bc37f532af35c4f38eb1986bbc5)) * **core:** Return user data with an __isError key as data in expressions ([#42490](https://github.com/n8n-io/n8n/issues/42490)) ([9fe595e](https://github.com/n8n-io/n8n/commit/9fe595e1b83179b54ac5768d03c5f90f9c409f16)) * **editor:** Show the workflow project under its name
How We Built Our Design Agent
How LangChain built Content Studio, an open-source design agent in Slack that makes on-brand videos, graphics and decks, and the harness that powers it.
n8n@3.0.0
# [3.0.0](https://github.com/n8n-io/n8n/compare/n8n@2.45.0...n8n@3.0.0) (2026-10-27) ### Bug Fixes * Add file-type tooltip to artifact download button ([#41490](https://github.com/n8n-io/n8n/issues/41490)) ([4a74f02](https://github.com/n8n-io/n8n/commit/4a74f027e612f31098b354dd3a77b2c2ab98281c)) * **ai-builder:** Check execution:read in Assistant execution reads ([#41863](https://github.com/n8n-io/n8n/issues/41863)) ([a967486](https://github.com/n8n-io/n8n/commit/a9674866d075e989a5ce896432262c9a7dc8b0c7)) * **ai-builder:** Send Assistant model calls through the configured AI transport ([#41948](https://github.com/n8n-io/n8n/issues/41948)) ([9b63561](https://github.com/n8n-io/n8n/commit/9b63561cbb48f06d216f27be3b77722d97e4e74a)) * Allow custom OAuth2 scopes ([#41249](https://github.com/n8n-
n8n@2.46.0
# [2.46.0](https://github.com/n8n-io/n8n/compare/n8n@2.45.0...n8n@2.46.0) (2026-10-27) ### Bug Fixes * Add file-type tooltip to artifact download button ([#41490](https://github.com/n8n-io/n8n/issues/41490)) ([4a74f02](https://github.com/n8n-io/n8n/commit/4a74f027e612f31098b354dd3a77b2c2ab98281c)) * **ai-builder:** Check execution:read in Assistant execution reads ([#41863](https://github.com/n8n-io/n8n/issues/41863)) ([a967486](https://github.com/n8n-io/n8n/commit/a9674866d075e989a5ce896432262c9a7dc8b0c7)) * **ai-builder:** Send Assistant model calls through the configured AI transport ([#41948](https://github.com/n8n-io/n8n/issues/41948)) ([9b63561](https://github.com/n8n-io/n8n/commit/9b63561cbb48f06d216f27be3b77722d97e4e74a)) * Allow custom OAuth2 scopes ([#41249](https://github.com/n8
Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon Bedrock
Every time an agent takes a step, it re-reads its entire context before acting. Here is how prompt caching for OpenAI models on Amazon Bedrock lets your agent remember, cutting input cost and latency without resending the same tools, instructions, and history every turn.
Context Engineering with Deep Agents: How To Remember Without Keeping Everything In Context
Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused.
How to solve dashboard debugging in six minutes with an MCP-connected AI agent
An AI agent used dbt lineage from inside the BI layer to answer three data mysteries in the time it takes to get coffee.
Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
arXiv:2610.31916v1 Announce Type: new Abstract: A durable agent can resume a consequential action while its authority changes and the action's external outcome remains uncertain. We study execution-time reliability of tool-using AI agents, isolating recovery decisions from language-model behavior. Checkpoint state, permission to dispatch, evidence of an external effect and permission to reconcile are distinct obligations. We present a finite conformance study that checks these obligations through histories scored against a provider effect ledger hidden from the recovering application. A prospectively frozen campaign executes 336 histories on native LangGraph and Temporal hosts, crossing 14 schedules, three provider contracts and four matched application policies. All 168 paired host cells
