Practical writing on agentic AI, LangGraph, LLM engineering, and the realities of building autonomous systems in production.
Production AI agents fail silently when inputs aren't validated and outputs aren't schema-enforced. Learn the three-tier validation model and structured output patterns that keep agents reliable.
Retries alone don't make AI agents reliable. Learn the full reliability stack—circuit breakers, graceful degradation, context budgets, and state recovery—that keeps production agentic systems running when dependencies fail.
Moving from single-agent to multi-agent architectures introduces coordination failures that don't exist in isolation. Here are the patterns that hold up in production.
State management is the unglamorous difference between a demo agent and a production agent. Here's how to implement checkpointing, persistence, and recovery in LangGraph systems that run at scale.
The right model for your agent isn't the most capable one — it's the one that handles your task class reliably at the lowest cost. Here's the framework engineering leaders use to make that call.
Vibes-based testing won't catch the failure modes that matter. Here's how engineering leaders build rigorous evaluation frameworks for AI agents before they go live — and how to keep them honest after.
LLM API costs, runaway loops, untracked invocations — AI agents can get expensive fast. Here's how engineering leaders build cost visibility and control into agentic systems before it becomes a CFO problem.
Fintech companies are drowning in compliance workflows, reconciliation, and support tickets. Agentic AI can change that — if you build it right. Here's what works.
When you have more than one AI agent, you need a conductor. Here's the supervisor pattern mid-market engineering teams consistently skip — and why it's costing them production reliability.
Shipping a traditional microservice and shipping an AI agent are fundamentally different problems. Here's the CI/CD architecture we use at Agentic Runbook — and why it works.
Before your company deploys an AI agent in production, legal needs a seat at the table. A practical framework for GCs and CCOs covering data privacy, liability, IP ownership, employment law, and sector-specific compliance — plus a 10-item pre-deployment checklist.
Most companies deploy AI agents in Engineering or Finance first. Meanwhile, the ops team — with the highest density of repetitive, rules-based workflows — is sitting untouched. Here's why that's a mistake, and which four workflows to fix first.
The transfer phase is where most AI agent projects fail. Learn the operational, technical, and organizational steps to hand off an agentic system so your team can maintain and extend it.
CFOs and Controllers at mid-market companies are deploying AI agents across month-end close, AP/AR, FP&A reporting, expense management, and audit prep. Here are 5 workflows that eliminate manual work — with implementation breakdowns and ROI benchmarks.
A 5-factor self-assessment framework to determine if your company is ready to deploy AI agents — with scoring rubric, common readiness gaps, and what to do next.
Most companies face a build vs buy decision when adopting AI agents. This framework gives CTOs and VPs of Engineering 5 concrete factors, a scoring matrix, and clear thresholds to make the right call on AI agent infrastructure.
A definitive guide to the 2026 production AI agent stack for CTOs and engineering leaders. Covers LLM selection, orchestration, observability, memory, tools, and deployment — with decision criteria and pitfalls for each layer.
A technical explainer on the three AI agent memory layers: short-term in-context state, long-term persistent checkpoints, and semantic vector retrieval. Includes LangGraph code snippets, architecture decision criteria, and testing guidance.
The 7 security risks specific to production AI agents — prompt injection, tool abuse, credential exposure, and more — with concrete mitigations for each.
LangGraph vs LangChain: a practical comparison for engineering teams building production AI agents. Covers stateful graphs, observability, memory, and when to use each.
Learn how COOs and VP Ops at $50M–$500M companies are building agentic AI operations teams — covering the 5 workflows AI agents handle best and how to measure impact.
Most AI POCs fail not because the technology doesn't work, but because the project was scoped wrong from the start. Here are the 5 mistakes that kill AI proofs of concept — and how to structure one that succeeds.
SaaS companies are deploying AI agents across support, onboarding, retention, internal ops, and code review. Here are 5 workflows that work in production — with before/after breakdowns and implementation guidance.
AI agent, chatbot, RPA, LLM—these terms get used interchangeably, but they mean very different things. Here's a plain-English breakdown of what an AI agent actually is, how it differs from the tools you already know, and when it's the right fit for your business.
AI agents fail in predictable ways — hallucination loops, state corruption, prompt drift, and more. Learn the 7 most common failure modes in production and the mitigation patterns that actually work.
Learn how to use LangSmith to trace, debug, and evaluate AI agents in production. Covers tracing setup, evaluations, prompt management, and what to monitor in multi-agent systems.
Step-by-step guide to building a multi-agent system with LangGraph, LangSmith, and Python. Covers state design, tool calling, agent nodes, and production observability.
Concrete agentic AI use cases for mid-market engineering teams — from customer operations to internal knowledge retrieval. Includes effort estimates and ROI signals.
Most AI agent budgets are wrong. Here's a realistic breakdown of build costs, timeline, and ROI metrics — from a team that's shipped them in production.
Most AI agents fail in production within 90 days. Here's the 5-step process engineering leaders use to build agents that survive — and the 4 failure modes to avoid.
The 'build vs buy' framing for AI agents is too narrow. Here's a four-option decision matrix that helps engineering leaders at mid-market companies make the right call — and avoid the hidden costs on both sides.
Most teams ship an AI agent with no idea if it's actually working. Here's a concrete evaluation framework—output quality, tool call accuracy, and end-to-end task success—built for production use in mid-market engineering orgs.
Agentic AI systems go beyond single-prompt answers. They plan, use tools, and execute multi-step tasks autonomously. Here's what that means in practice — and why it matters for mid-market engineering teams.