LangChain

gemini decisions API launched today! this API will feel very familiar if you've used jev or other decision models. gemini decisions is much faster and cheaper than an LLM, and outputs are structured + reliable :) we have day 0 support in langchain: docs.langchain.com/oss/pytho…

here's my "proactive agent engineering" talk slides! thesis: there's a shift coming from "you give an agent tasks" to "an agent gives you tasks" this requires a step change in memory and ux docs.google.com/presentation…

i messed up the code block formatting in my blog on "browser use for deep agents" the design agent we tested browser use in fixed it, then used browser use to validate the change. great example of how browser use enhances testing, very full circle! xitter.twitterapi.xyz/sydneyrunkle/status/21…

full transparency, things that went wrong during the filming of this demo... - almost booked a reservation that required a HUGE downpayment / cancellation fee bc my card was saved on resy, so the agent could just book+pay - hit rate limits w/ resy bc i simulated log in so many times trying to get the demo right browser use is a relatively new primitive for agents, so it makes sense that we're working through some growing pains the hardest things rn seem to be guardrails + working at scale

this piece is worth the read, but quite dense. here's my (relatively) brief breakdown: super agents are defined by 4 characteristics: - continuous: one agent across threads and access points - productive: responds quickly and completes work efficiently - proactive: responds to events in its environment - persistent: durable execution for high availability and crash recovery these constraints apply to personal agents and agents doing commercial work alike, so the underlying architecture should look similar the primitives behind them: - two level architecture: a router layer keeps interaction continuous, and a worker layer runs tasks concurrently - event driven design: the agent reacts to events in its environment and can schedule runs toward a long term goal - multi channel interaction: context carries across channels, and async execution keeps the agent productive - separate compute, state, and environments: the "separate the brain from the hands" idea, which supports persistence and security and lets the system scale - watchdog: an independent control loop that keeps the whole thing running they're upfront about what's still open: - reliable scale - representing tasks and work without the "session" primitive - agent to agent communication and context standards - security and privacy, as always their core claim is that models need less and less human intervention on individual tasks, so the bottleneck to further productivity gains is shifting to human attention which raises the question i'm most interested in: how do we design human-agent collaboration when human attention is the bottleneck?