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Eager to apply more sophisticated agentic memory to the largest conversational benchmark, LongMemEval, we discuss the benchmark, our approach, our somewhat disappointing state of the art findings, and the need for a more comprehensive benchmark for agentic memory than LongMemEval.

LongMemEval stands out as a strong benchmark for long-term memory, but our RAG-like methods’ success shows it may not capture all aspects, pointing to the need for further benchmark development.

Emergent AI agents revolutionize cybersecurity by automating threat detection, compliance, and SOC operations, enabling resilient and proactive defense.

An empirical study of how different Generative Foundation Model pairings impact agent creation, verification, and emergent system behaviors across 40 enterprise tasks.

We benchmarked the latest LLMs from OpenAI, Anthropic, Deepseek, and Google within our Data Insights Agent framework to identify which delivers the most accurate, fastest, and most consistent insights.

We propose a first-principles architecture for agentic systems, inspired by Unix and Kubernetes, with nine core abstractions enabling runtime creation, delegation, and async execution.

Discover why standard AI benchmarks fall short for enterprise needs and how agent performance is truly measured on realistic, multi-application workflows using both UI and APIs.

A structured five-layer framework provides standardized benchmarking for AI agent capabilities across the full spectrum of enterprise task complexity, from UI to infrastructure.

Emergence’s Agents platform is evolving into a system that can automatically create agents and assemble multi-agent systems withminimal human intervention.

With the explosion of user-generated content, moderating digital conversations has become critical in keeping online platforms welcoming and respectful.

Connecting downstream enterprise applications to the world of external services is crucial, but navigating the vast API landscape can feel like exploring an uncharted jungle.

Since our Multi-Agent Orchestrator launch in December 2024, we’ve introduced new features and refined capabilities to streamline operations, strengthen compliance, and support evolving enterprise needs. Below are the highlights.

‍Today we are announcing the Emergence Orchestrator, an autonomous meta-agent that coordinates and manages interactions between AI agents across enterprise systems.

As enterprises increasingly embrace AI agents to drive productivity and streamline operations, a pivotal question emerges: How do we ensure these agents deliver reliable, compliant,

This whitepaper examines the role of benchmarking in enterprise AI adoption, addressing reproducibility, bias, and applicability while outlining strategies for scalable benchmarks.

A new paper by researchers from Emergence, Openmind Research Institute, and Sakana AI explores how to encode specific innate knowledge into AI systems that are agnostic to the sensory modality of their inputs.

A new paper, "Transformer Layers as Painters," co-written by Emergence researchers and Sakana AI, investigates the internal workings of transformers, focusing on how the removal or reorganization of information impacts pretrained models.

Intelligent agents are showing promise in transforming interactive software by improving multi-step task automation significantly across diverse digital environments.

As we’ve seen the rapidly rising impact of LLMs, we’ve also seen the growing importance of “synthetic data,” generated instructional raw text used to train LLMs for specific tasks without the need to mine from real human conversations.

Emergence is a compelling phenomenon observable both in natural systems and in engineered designs, where complex behaviors and patterns arise from simple interactions.

In this post, we consider how to make language models better, not just faster, inspired by several papers.

Skill harvesting allows agentic systems to self-reflect, autonomously developing more specialized skills.

At Emergence, we’ve always believed that the next significant advancement in workflow automation will come from the planning, selection, and use of multiple external tools by artificial intelligence.

Our everyday interactions with computers are filled with slow and repetitive tasks.

A number of enterprise workflows involving language- and tool-control tasks can be augmented with LLM- and LVM-powered agents.

Self-improving agents have varying objectives, and the issue of aligning them with human values is critical.

The pivotal advancement in the ability of computers to understand language and develop functional world models has profoundly reset the landscape in computing.

The high accuracy and precision of our model represent a new achievement in reliably identifying unsuitable prompts and biased datasets.

The concept of a software agent can be traced back to the model Hewitt, et al.