agents
Cloud infrastructure expenses have surged, with generative AI agent API costs tripling in one quarter. Although these applications succeed with users, their operational run-rates severely erode profit margins. When leadership suggests migrating to cheaper models, engineering teams often resist. Past cost-cutting attempts in staging environments caused hallucinations, formatting
agents
Utilizing high-precision recall and low-latency performance to drive interactive experiences across deep conversation archives.
agents
Why conversational systems require pointers over payloads
agents
There is a significant gap between the way automated systems handle task assignment and the reality of human interaction. While we anticipate that proactive AI can accurately identify "action items" from our discussions to oversee workflows, traditional software often fails to account for the nuances of Social Communication.
agents
Engineering teams mostly ignore their LLM and agent telemetry by treating it like traditional software logs: a depreciating asset to be discarded. In the world of legacy software, logging serves primarily as a cost center. We record stack traces and database timeouts to squash bugs, then rely on short retention
agents
The development of reliable AI agent systems has closely mirrored traditional software engineering over the past two years. Carrying out complex, long-term tasks, such as thorough codebase audits or large-scale platform migrations, necessitated the creation of dedicated pipelines. Developers were tasked with establishing the rules, mapping the workflows,
AI
Designing Delightful Agent Experiences Without Breaking Safety Boundaries
agents
An agentic approach to building context to achieve reliable conversational intelligence. Conversational AI agents often struggle to reason smoothly across long-horizon conversations because their contexts are disconnected. For instance, if someone says, "I'm tired," today, the agent treats it as an isolated comment without considering