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Agent UX Patterns That Work
Users interacting with agents have different needs than users interacting with traditional software. Agents think, which takes time. Agents take actions, which carry consequences. Agents make mistakes...
Agents That Generate UI
The standard agent interface is text in, text out. Users type messages, agents respond with text. This works for many cases but ignores that some information is better conveyed through structured inte...
Client-Side Tools
Most agent tools run on servers. The agent requests an action, the server executes it, results return to the agent. But some operations need to happen where the user is - accessing local files, using ...
Building Custom Apps for Your Agents
Pre-built tools cover the common cases - web search, document processing, image generation, standard API integrations. But every organization has unique systems, proprietary APIs, and domain-specific ...
Workflows vs Agents: When to Use Each
Workflows are predetermined sequences; agents make runtime decisions. The distinction matters because most production AI systems need both. Explore the inference.sh runtime →
Building a Research Agent
Research tasks are among the best applications for AI agents. They involve gathering information from multiple sources, synthesizing findings, and producing structured output - exactly the kind of mul...
Tool Approval Gates
Approval gates put humans in control of consequential agent actions—allowing agents to propose while requiring confirmation before execution. See approval gates in action →
Sandboxed Code Execution for AI Agents
The most powerful agents can write and execute code. This capability transforms agents from systems that can only use predefined tools into general-purpose problem solvers. Need to analyze a dataset? ...
Real-Time Agent Streaming
Ten seconds with a blank screen feels like a minute. The same ten seconds with visible progress feels reasonable. Real-time streaming transforms perceived responsiveness by showing users what's happen...
Debugging AI Agents in Production
Something went wrong. A user reports unexpected behavior. An automated monitor fires an alert. A customer complains. You need to figure out what happened, why, and how to prevent it from happening aga...
Concurrent Agent Execution
Sequential execution is the default mode for most agent systems. The agent thinks, calls a tool, waits for the result, thinks again, calls another tool. Each step follows the previous in a linear chai...
When to Use Multi-Agent Systems
Multi-agent systems sound impressive. Multiple AI agents collaborating on complex problems, dividing work, combining expertise. The reality is that multi-agent architectures add substantial complexity...
The Real Cost of Agent Infrastructure
The visible costs of building agents (API fees, frameworks) are dwarfed by the hidden costs: state management, auth, observability, and the infrastructure to run reliably in production. See what the r...
Agent Memory That Actually Works
Every conversation with an agent that can't remember feels like starting over. Effective agent memory requires more than storing conversation history—it's about what to store, how to retrieve it, an...
From Demo to Production
Every agent starts as a demo. You prototype something, it works on your laptop, you show it to stakeholders, they are impressed. Then comes the question that changes everything: can we ship this? The ...
The Tool Integration Tax
An agent without tools is just a chatbot. Tools are what transform LLMs from conversation partners into systems that take action, retrieve information, and interact with the world. But every tool an a...
Built-In Agent Observability
Observability bolted onto agents after the fact creates gaps and adds complexity. Building it into the runtime from the start produces fundamentally better visibility with less effort. See built-in ob...
Hierarchical Agent Delegation
Multiple agents working together can accomplish more than a single agent working alone. The question is how to organize that collaboration. After watching many multi-agent systems succeed or fail, a c...
Human-in-the-Loop for AI Agents
Agents that take real action are powerful—and dangerous when they take the wrong action. Human-in-the-loop patterns keep humans in control of consequential decisions without sacrificing automation. ...
Durable Execution for AI Agents
When your agent crashes mid-task, does it lose all progress? Durable execution uses checkpoints to make agents resilient to failures, network issues, and process restarts. See durable execution in act...
Why Agent Runtimes Matter
A demo agent takes an afternoon to build. A production agent takes months of infrastructure work. The runtime layer handles durability, observability, and tool orchestration so you can focus on agent ...
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