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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
Blog card for Documentation Drift Breaks Coding Agents

Documentation Drift Breaks Coding Agents

Software documentation tools now shape coding-agent behavior; stale repo docs, AGENTS.md files, and runbooks can send agents into the wrong code path.
Blog
Focused Labs blog card for Agent Prompt Caches Are a Runtime Boundary

Agent Prompt Caches Are a Runtime Boundary

Prompt caching makes context order, provider cache controls, and cache-hit telemetry part of agentic AI architecture.
Blog

Your RAG Pipeline Hallucinates Because It Never Checks Its Own Work

Build a corrective RAG pipeline with LangGraph that grades retrieval quality, rewrites bad queries, and generates cited answers.
LangChain
Blog card for Approval Queues Are the Runtime for Agentic AI Workflows

Approval Queues Are the Runtime for Agentic AI Workflows

Approval queues turn human-in-the-loop AI from a button into durable runtime state, with policy, checkpoints, ownership, traces, and receipts.
Blog
Focused Labs blog card for AI Incident Management Breaks Without a Shared Record

AI Incident Management Breaks Without a Shared Record

AI incident management works when agents maintain a shared record of evidence, decisions, owners, approvals, and follow-up work, not just plausible root causes.
Blog
AI Agent Evaluation Ends Too Early

AI Agent Evaluation Ends Too Early

AI agent evaluation has to keep running through traces, online evaluators, human review, datasets, and redeploy gates after release.
Blog
Enterprise AI Agents Have a Control Plane Now social card

Enterprise AI Agents Have a Control Plane Now

Enterprise AI agents are becoming an operating estate, so registry, identity, policy, observability, cost, approvals, and retirement need a control plane.
Blog
Blog card for Agent Handoffs Turn Routing Into Runtime State

Agent Handoffs Turn Routing Into Runtime State

Agent handoffs are runtime ownership transfers. Treat multi-agent routing as state, receipts, traces, and contracts before teams trust it with real work.
Blog
Blog card for MCP Security Starts After Tool Approval

MCP Security Starts After Tool Approval

MCP security starts with approval, but it only holds when runtime checks catch tool drift, changed schemas, new effects, and missing call evidence.
Blog
Focused Labs social card for AI Agent Governance Runs Before the Tool Call

AI Agent Governance Runs Before the Tool Call

AI agent governance becomes useful when policy executes before the tool call, while the runtime still has enough context to block, approve, recover, or write a receipt.
Blog
Focused Labs social card for AI Agent Cost Is a Runtime Signal

AI Agent Cost Is a Runtime Signal

Agent spend has to be controlled where agents actually spend money: in traces, model routes, retries, tools, evals, and harness policy.
Blog
Enterprise AI Agents Are Leaving the Server

Enterprise AI Agents Are Leaving the Server

AI agent integration now crosses the client runtime. App state, approvals, permissions, and frontend traces decide whether enterprise agents can act safely.
Blog
Blog card for AI Agent Governance Follows the Execution Path

AI Agent Governance Follows the Execution Path

AI agent governance belongs in the runtime path, where identity, policy, traces, and approvals meet before production side effects.
Blog

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