Building at the frontier of agentic AI
We build AI products and publish what we learn along the way. Research, tools, and open knowledge from the cutting edge.
What we're exploring
Our research focus
We build products and conduct research across several areas of agentic AI. Here's what we're working on.
Autonomous Agents
Agents that reason, plan, and execute multi-step tasks with minimal human oversight. We study how to make them reliable enough for production.
Multi-Agent Orchestration
Coordinating teams of specialized AI agents that collaborate, debate, and synthesize — the architecture behind Kapwa's Symphony Mode.
Long-Horizon Research
Deep research agents that work over days and weeks, building on their own findings to produce continuously evolving analysis.
Applied AI Engineering
Turning research into shipped products. Streaming architectures, semantic memory, tool use, and the engineering that makes AI systems work.
See agentic AI analysis
in action
Describe any work or business scenario and watch our AI break it down — identifying agent opportunities, architectures, and implementation paths in real time.
Our Products
Kapwa
Our flagship AI product — an advisor platform where users select from 288+ specialized personas — historical figures, domain experts, and fictional strategists — for multi-perspective conversations powered by ensemble AI orchestration.
What's next
Long-horizon deep research
We're building a research agent that doesn't stop after one answer. It produces a report, then continues working — running deeper analysis, finding new connections, and updating its findings daily. Research that compounds over time.
Continuous research reports
Imagine a research report that updates itself. The agent performs an initial deep dive, delivers findings, then keeps working in the background — running increasingly complex analyses that build on previous results. Each day, the report gets deeper and more nuanced.
Reading List
What we're reading
Weekly Gen AI headlines for builders, plus the papers that define the field.
Anthropic Releases Claude Opus 4.6 With Agent Teams That Split and Parallelize Complex Tasks
Opus 4.6 lets you assemble teams of agents that coordinate in parallel. API users also get compaction for longer-running agentic workflows.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Trained for an estimated $6 million, DeepSeek-R1 matched OpenAI o1's reasoning capabilities and was released under the MIT license. Validated that frontier-level reasoning can be achieved through RL without expensive supervised fine-tuning, fundamentally altering the economics of AI development.
Mixtral of Experts
Demonstrated that mixture-of-experts architectures can match models 6x their active parameter count. By activating only a subset of parameters per token, MoE models achieve large-model quality at small-model inference cost — a key efficiency breakthrough.
Learn
What we've learned
Notes, frameworks, and explanations from our research and product work. Written to be useful, not to sell.
What is AI (Without the Jargon)
A plain-English explanation of AI, machine learning, and large language models — no jargon, no hype.
Read moreWhat are AI Agents
From chatbots to autonomous agents — what makes an agent different and why it matters.
Read moreThe Agentic AI Framework
A structured approach to identifying where AI agents create the most value in any operation.
Read more