Endava Rebuilds Software Delivery Around AI Agents

Google DeepMind introduced Gemma 4 12B, a multimodal AI model designed to run on consumer laptops with 16GB of RAM. The model uses an encoder-free architecture that processes vision and audio inputs directly into the language model backbone, reducing latency and memory overhead. Performance approaches the larger 26B model while maintaining a smaller footprint, and it is released under an Apache 2.0 license.

Researchers from UIUC, UC Berkeley, and Chroma released Harness-1, a 20-billion parameter open-source search agent that scores 73% on information recall benchmarks, outperforming GPT-5.4 (70.9%) and other proprietary models. The model is available under Apache 2.0 license on Hugging Face. Harness-1 achieves its performance by offloading search session management to a structured software environment rather than relying on expanded context windows, suggesting that model efficiency matters more than raw parameter size for autonomous retrieval tasks.

OpenEnv, a tool for building agentic execution environments, is transitioning to community governance with a steering committee that includes Meta, Nvidia, Hugging Face, and others. The project is being repositioned as a protocol layer for standardizing how RL environments are published and consumed by agents, rather than dictating reward frameworks or training logic. This move aims to enable open source models to achieve the same training efficiency that frontier labs achieve by co-optimizing models with their execution harnesses.

AI agents deployed across enterprises fail to share corrections and learnings between team members, creating isolated versions of the same tool that never sync. Asana and other platforms are building shared memory architectures to solve this problem, but the challenge of storing, controlling, and maintaining consistency across multi-agent workflows remains largely unsolved. According to Asana research, 75% of knowledge workers use AI on the job, yet only 5% of companies report productivity gains, partly because agents lack enterprise context and shared learning.