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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
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

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