Loading post…
MongoDBAnnouncement
Closing the Gap Between Agentic Promise and Impact with Industry Leading Retrieval
Summary
McKinsey projects AI agents will generate $2.9 trillion annually; a WRITER survey finds 97% of executives have deployed agents, yet only 29% see significant ROI due to data accuracy issues. MongoDB announced capabilities embedding benchmark-leading retrieval directly into its operational database, closing the gap between agentic promise and impact. Automated Embedding in MongoDB Atlas powered by Voyage AI, now GA, uses the top-ranked Voyage 4 model family for accurate retrieval without separate pipelines, as demonstrated by MedINT. The Atlas Embedding and Reranking API and voyage-code-4 model, designed for agentic coding retrieval, extend accurate retrieval to any stack and coding agent workloads at low cost. $vectorSearch in Atlas Stream Processing enables agents to retrieve accurate data in motion, and a joint MongoDB-Temporal reference architecture provides a forkable starting point for durable execution on live data.
Context
The post discusses the gap between agentic AI promise and real-world impact, citing surveys showing low ROI despite high deployment. It announces MongoDB’s new capabilities that embed accurate retrieval directly into the operational database, including Automated Embedding, the Atlas Embedding and Reranking API, voyage-code-4, and $vectorSearch in Atlas Stream Processing, along with a joint reference architecture with Temporal.
Approach / What changed
MongoDB embeds benchmark-leading retrieval directly into its operational database via Automated Embedding powered by Voyage AI, the Atlas Embedding and Reranking API, the voyage-code-4 model for coding agents, and $vectorSearch in Atlas Stream Processing. It also provides a joint reference architecture with Temporal for durable execution on live data.
Takeaways
Only 29% of organizations see significant ROI from generative AI despite 97% deployment, with data accuracy and operational complexity as key barriers.
Automated Embedding in MongoDB Atlas powered by Voyage AI provides top-ranked retrieval without separate embedding pipelines, as demonstrated by MedINT’s 40-million-document corpus.
The joint MongoDB-Temporal reference architecture enables agents to resume interrupted workflows from the last completed step, keeping vector stores in sync with source data changes.
FAQ
What is the main barrier to agentic AI impact?
Data accuracy and operational complexity are key barriers; only 29% of organizations see significant ROI from generative AI despite high deployment rates.
How does Automated Embedding in MongoDB Atlas work?
It uses Voyage AI models to generate embeddings automatically when creating a vector search index, eliminating the need for separate embedding pipelines and keeping data in sync.
What is voyage-code-4 designed for?
It is designed for agentic coding retrieval workloads, supporting flexible output dimensions (256d to 2048d), 32K input context, and quantization-aware training at low cost.
Source and freshness
- Source
- mongodb.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
Keep reading
Related reading
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
The best engineering reads, once a week
One useful email. Unsubscribe anytime.