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Blog card for Agent UI Is Runtime Infrastructure

Agent UI Is Runtime Infrastructure

Token streaming makes an agent look alive. Typed event streams make agent products usable, inspectable, recoverable, and safe enough to run inside real workflows.
Blog
Focused Labs blog card for Agent Failures Should Open Tickets

Agent Failures Should Open Tickets

Recurring AI agent workflow failures should become named issues with linked traces, owners, regression evals, and release evidence.
Blog
Focused Labs social card for Agent Traces Need to Cross the MCP Boundary

Agent Traces Need to Cross the MCP Boundary

Agent observability breaks when traces stop at the MCP tool boundary. Pass W3C trace context through MCP to connect planner, tool, and service spans.
Blog
Agentic AI Implementation Runs Through Change Control social card

Agentic AI Implementation Runs Through Change Control

Enterprise agent rollouts need a change-control spine: workflow ownership, permission envelopes, eval evidence, trace samples, and rollback paths.
Blog
AI Agent Orchestration Needs Receipts social card

AI Agent Orchestration Needs Receipts

Agent orchestration needs a side-effect ledger that records operation keys, receipts, retries, compensation, and ownership before mutating tools touch production systems.
Blog
Blog card for Agent Benchmark Scores Are Measuring the Harness, Not the Model

Agent Benchmark Scores Are Measuring the Harness, Not the Model

Agent benchmarks do not just measure model capability. They measure the harness, runtime limits, tools, retries, and observability wrapped around the model.
Blog
Blog card for AI Agent Authentication Starts With Workload Identity

AI Agent Authentication Starts With Workload Identity

AI agent authentication starts with workload identity, scoped credentials, and runtime-owned delegation so tool calls have authority, audit evidence, and expiration.
Blog
Blog card for Agentic AI Architecture Needs Model Routing

Agentic AI Architecture Needs Model Routing

Agentic AI architecture needs model routing, telemetry, and policy so production agents can send each workload to the right model instead of one default.
Blog
Blog card for Stop Eager-Loading MCP Tools Into the Context Window

Stop Eager-Loading MCP Tools Into the Context Window

MCP servers should not eagerly load every tool schema into an agent's context window. Lazy-load tools by intent, then govern and audit execution.
Blog
Blog card for MCP Is Packaging. Agent-Operable Interfaces Are the Product article

MCP Is Packaging. Agent-Operable Interfaces Are the Product

MCP packages tools, but the real product is the narrow, typed, auditable interface an agent can actually operate.
Blog

LangGraph Error Handling Patterns for Production AI Agents

Build reliable LangGraph agents with production error handling. Implement retry logic, fallback strategies, and graceful degradation.
Use Cases

LangGraph for Enterprise Agent Development: Why We Built Our Entire Practice on It

Why Focused chose LangGraph as the foundation for enterprise AI agent development. Deterministic workflows, Deep Agents, observability with LangSmith, and eval-driven development in production.
LangChain

Evaluation Pipelines for LangGraph Agents

Build evaluation pipelines for LangGraph agents using LangSmith. Learn how to create datasets, run LLM-as-judge evals, and detect regressions before they reach production. Ship agent changes with confidence, not guesswork
Use Cases

Multi-Agent Orchestration in LangGraph: Supervisor vs Swarm, Tradeoffs and Architecture

Build multi-agent systems in LangGraph using supervisor and swarm patterns. Compare routing accuracy, latency, and real production tradeoffs, with implementation details and failure modes you’ll actually hit.
Use Cases

How to Build a PDF RAG Pipeline Without Text Extraction (Using Native PDF Embeddings)

Build a RAG pipeline that embeds raw PDF bytes with Gemini Embedding 2. No text extraction for embedding, vision-model OCR for the text you need, and better retrieval than extract-then-embed
Use Cases

What We Learned at O11yDay NYC

Our key takeaways from O11yDay NYC on observability, evals, and understanding your systems in production.
Blog

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