Featured
Your AI agents need a terminal, not just a vector database
DCI lets AI agents grep, trace, and verify data directly — no embeddings needed. Researchers say it's faster and cheaper than vector search for complex tasks.
Ben Dickson
A 0.12% parameter add-on gives AI agents the working memory RAG can't
A new memory module lets AI agents retain context across long interactions — adding just 0.12% of model parameters with no architectural changes.
Ben Dickson
Google's Managed Agents API promises one-call deployment at the cost of execution layer control
Google's new Managed Agents API promises to collapse weeks of deployment work into one call. The catch: it hands Google the execution layer.
Emilia David
Enterprise AI agents keep failing because they forget what they learned
Most enterprise AI agents never make it out of the pilot phase. The problem isn't the model — it's that agents forget what they learned.
Taryn PlumbSubscribe to get latest news!
Deep insights for enterprise AI, data, and security leaders
NanoClaw's creators are turning the secure, open source AI agent harness into an enterprise 'second brain'
As AI shifts from a novelty tool that answers questions into a digital workforce that autonomously executes tasks, NanoCo AI is betting that verifiable security will be the defining metric of success.
Carl Franzen
Claude agents can finally connect to enterprise APIs without leaking credentials
Self-hosted sandboxes and MCP tunnels move credential control to the network boundary — here's what the architecture means for teams deploying agents against internal systems.
Emilia David
LangSmith Engine closes the agent debugging loop automatically — but multi-model enterprises still need a neutral layer
LangSmith Engine automates the full agent fix loop — detecting failures, diagnosing causes and drafting PRs. But multi-model enterprises say a neutral observability layer still wins.
Emilia David
Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search.
Intercom, now called Fin, launches an AI agent whose only job is managing another AI agent
The company formerly known as Intercom just did something that no major customer service platform has attempted at scale: it built an AI agent whose sole job is to manage another AI agent.
How RecursiveMAS speeds up multi-agent inference by 2.4x and reduces token usage by 75%
A new framework from UIUC and Stanford lets AI agents share embeddings instead of text — slashing token usage and cutting training costs by more than half.
Ben Dickson
Claude’s next enterprise battle is not models: it’s the agent control plane
That would put Anthropic in a more direct fight with OpenAI and Microsoft — not just over model quality, but over the operating layer of AI agents.
Carl Franzen
Claude Code's '/goals' separates the agent that works from the one that decides it's done
Coding agents lie about being finished. Claude Code's new /goals command adds a second model whose only job is to decide when the work is actually done.
Emilia David