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Evaluating Voice Agents with LangSmith: Execution, Outcomes, and Experience

A practical look at evaluating voice agents with LangSmith across execution, real-world outcomes, latency, audio quality, and conversational experience.
LangChain

Benchmarking LLMs for Structured Data Extraction

Which model is best for structured document extraction depends on what you optimize for. We tested Gemini, Claude, and OpenAI on handwritten forms and compared accuracy, exact match, and cost using LangSmith.
Blog
Agentic Software Engineering Makes Human Context the Budget

Agentic Software Engineering Makes Human Context the Budget

Faster implementation makes human context, validation, review, and ownership the real budget for software teams working with coding agents.
Blog
AI Agent Governance Gets Audited in the Trace social card

AI Agent Governance Gets Audited in the Trace

AI agent governance gets real when traces capture tool calls, policy decisions, human oversight, monitoring, and audit evidence.
Blog
AI Agent Testing Runs on Failure Traces

AI Agent Testing Runs on Failure Traces

Agent tests get useful when a live failure becomes an executable eval with a verifier, an owner, and a release gate.
Blog
AI Agents in Finance Live or Die by Runtime Receipts

AI Agents in Finance Live or Die by Runtime Receipts

AI agents in finance earn trust when runtime receipts connect cost, authority, approvals, side effects, and outcomes to each work item.
Blog
Agent Protocols Fail at the Seams

Agent Protocols Fail at the Seams

MCP, A2A, and ACP simplify agent integration, but security ownership sits in the runtime where content, authority, and state cross protocol seams.
Blog
AI Agent Infrastructure Is Splitting at the State Layer

AI Agent Infrastructure Is Splitting at the State Layer

The model loop gets the attention. The state layer holds the checkpoints, memory, identity, traces, policy decisions, and audit history that make agents durable.
Blog
Agentic Workflows Should Get Less Agentic

Agentic Workflows Should Get Less Agentic

Agentic workflows are supposed to get boring. Repeated behavior should move into deterministic execution while traces decide when drift sends work back to an agent.
Blog
Focused Labs article card for Multi-Agent Systems Break at the Collaboration Plane

Multi-Agent Systems Break at the Collaboration Plane

Multi-agent systems fail when shared collaboration state disappears. Build a traceable collaboration plane for claims, findings, handoffs, and evals.
Blog
Enterprise AI Agents Are Runtime Products

Enterprise AI Agents Are Runtime Products

Enterprise AI agents become manageable when the model, sandbox, policy, traces, evals, credentials, and audit receipts share one owned runtime boundary.
Blog
Focused Labs social card for Agent Traces Rewrite the Harness

Agent Traces Rewrite the Harness

Agent traces only matter when they become eval cases, harness patches, release gates, and proof that live failures stayed fixed.
Blog
OG card for AI Agent Security Happens at the Tool Call

AI Agent Security Happens at the Tool Call

AI agent security belongs at the runtime-owned tool-call boundary where principal, grant, resource, capability, data flow, and audit get checked.
Blog
Blog card for Coding Agent Spend Belongs in the Trace

Coding Agent Spend Belongs in the Trace

Coding-agent spend becomes manageable when cost is attached to traces, reviewed by engineering, and enforced through gateway policy.
Blog
Blog card for Agent Orchestration Belongs in Code

Agent Orchestration Belongs in Code

Agent orchestration works better when loops, retries, fanout, and approvals move from prompts into executable code with a narrow harness boundary.
Blog
AI Agent Observability Runs on Conversation IDs

AI Agent Observability Runs on Conversation IDs

Agent observability gets useful when one conversation ID follows the agent through model calls, tools, APIs, queues, databases, and eval loops.
Blog

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