Learn to build next-generation AI agents with Jamba in this new course from DeepLearning.AI and AI21 Labs.
We're thrilled to announce a groundbreaking new course – Build Long-Context AI Apps with Jamba – created in partnership with Andrew Ng's DeepLearning.AI and AI21 Labs. This course equips developers with the tools to build the next generation of AI agents using Jamba's revolutionary architecture, showcasing how developers handle long-context applications.
Through our work developing language models and observing their real-world applications, we at AI21 Labs have gained deep insights into the limitations of current architectures. As enterprises start experimenting and deploying AI agents for complex tasks, we've identified critical bottlenecks in existing models that particularly impact these agents' effectiveness.
Why does context length matter so much for agents? AI agents need to maintain extensive operational memory to perform complex tasks effectively. They must simultaneously hold conversation history, reference materials, task instructions, and intermediate results while working toward their objectives. Traditional models force agents to constantly "forget" critical context, leading to inconsistent performance and broken chains of reasoning.
Traditional transformer-based architectures, while powerful, struggle with the demands of modern AI agents. Their quadratic computational scaling makes processing long contexts prohibitively expensive, forcing developers to fragment documents and conversations artificially. This fragmentation breaks the very contextual understanding that makes agents effective.
Recently, Mamba architecture emerged as a potential solution, promising linear scaling through selective state-space models. However, our extensive testing revealed that Mamba's efficiency comes at a significant cost to contextual understanding and output quality – precisely the capabilities agents need most.
This deep understanding of both architectures' limitations led us to develop Jamba – a hybrid architecture that combines the transformer's precise attention mechanisms with Mamba's efficient processing. Our commitment to pushing the boundaries of context length has paid off: Jamba leads the industry with the highest effective context length according to NVIDIA's independent ruler benchmark – a critical advantage for AI agents that need to maintain extensive operational memory while delivering consistent, high-quality outputs. For AI agents, this means:
Jamba was engineered specifically for enterprise applications where agents need to process complete information landscapes. Instead of the piecemeal approach required by traditional models, Jamba enables agents to:
Led by AI21 Labs experts Chen Wang (Lead Alliance Solution Architect) and Chen Almagor (Algorithm Team Lead), this hands-on course equips you with the tools and skills to build sophisticated AI agents using Jamba, focusing on:
This course is designed for developers with:
By the end of the course, you'll be able to:
As enterprises increasingly rely on AI agents for complex tasks, the need for architectures that can handle full-context processing becomes paramount. This course equips developers to build the next generation of AI agents that can truly understand and operate within the full scope of enterprise information landscapes.
Ready to build AI agents that meet the demands of modern enterprises?
Enroll now and start transforming how you handle long-context applications with Jamba.