Blog
Design, implementation, evals, rollout, observability, and day-two operations for production agent systems.
20 articles
A measurement model for AI agent ROI: baselines, a cost side that includes LLM usage and review time, pilot metrics, and expand-or-kill decision rules.
Where failed AI agent projects actually bleed money: incidents, rework, CRM pollution, security debt, organizational distrust - and how to prevent or recover.
On Aug 26, 2026 OpenAI shuts down /v1/assistants, /v1/threads and /v1/runs. What errors out, why it is not an endpoint swap, and a 1-2 week migration path to the Responses API.
Production logging policy for AI agents: minimum event schema, redaction, retention, OTel mapping, and the incident reconstructability bar.
How to introduce agents to teams: ownership, training, policies, transparency, and handling “the agent is wrong”.
Build a golden set, score tools and policy, set a pass bar, and run adversarial tests before production. A stack-agnostic acceptance harness for business agents.
How to retry agent tool calls without double emails, double charges, or duplicate CRM records.
How to pick models for ops agents and control spend: routing, caching, budgets, and fallbacks.
Start with one tool-using agent. Add multi-agent only for real role, privilege, or parallel splits you can own - decision table, checklist, and cost trade-offs.
LangChain, CrewAI, AutoGen, n8n, and hosted assistant APIs - when they speed you up and when they add glue risk.
When agents need RAG, how to chunk and cite sources, what to store in memory, and how to keep policies fresh.
How to roll out agents safely: shadow predictions, canary percentages, prompt versioning, and rollback.
Treat prompts and tool schemas like code: review, version, canary, rollback, and release notes for operators.
How agencies should orchestrate AI agents for client delivery without overbuilding multi-agent theater.
A one-week discovery audit to decide where agents belong before you build.
A practical template to scope an AI agent pilot: workflow, tools, gates, acceptance test, exclusions, and owners.
A practical approach to embedding agents in Gmail, CRM, docs, and chat - instead of forcing a new platform day one.
A practical workflow-mapping method for production agents: steps, tools, failure cost, and where humans stay in control.
Practical operational metrics for agent systems: cycle time, error rate, rework, gate reject rate - not empty 'AI adoption' scores.
Production AI agent failure modes operators actually hit: tool misuse, stale context, missing gates, silent degradation. Design controls and detection - with primary sources.