Building Effective AI Agents
The most-cited engineering post on agents: five workflow patterns and when a true agent is warranted.
Open anthropic.com →A workflow follows steps you defined with the model filling in the reasoning slots; an agent decides its own path with tools in a loop. Workflows are cheaper, faster, and debuggable, and they cover most real product needs (tagging, drafting, summarising, extracting); agents earn their complexity only when the path genuinely cannot be known in advance. Both OpenAI and Anthropic say the same thing in their own guides: start with the simplest thing, add autonomy only when a measured need appears.
A quick orientation. The real value is below: resources worth your time, from people who've actually done it.
The most-cited engineering post on agents: five workflow patterns and when a true agent is warranted.
Open anthropic.com →OpenAI's distilled deployment lessons: use-case selection, guardrails, and orchestration patterns.
Open openai.com →The manifesto that successful agents are mostly well-engineered software with LLM sprinkled at key points.
Open github.com →A working engineer's decision criteria with cost and reliability tradeoffs spelled out.
Open towardsdatascience.com →Short and sharp: if the steps are known in advance, a workflow wins on every axis.
Open redis.io →A neutral reference explainer to align your team on terminology before the build debate.
Open promptingguide.ai →How a regulated enterprise makes the call, useful discipline for any founder.
Open medium.com →Brings the production data: only 23% of orgs have scaled an agent anywhere.
Open intuitionlabs.ai →The founder-flavoured argument against reaching for the harder, smarter-sounding option.
Open buildmvpfast.com →The LangChain founder on what separates agent demos from agents in production.
Open sequoiacap.com →Current thinking on trust and reliability, the two things that kill agent features.
Watch on YouTube youtube.com →A stack-level tour of what you need around the model to make agents dependable.
Watch on YouTube youtube.com →The Sierra founder on where agents genuinely outperform, from thousands of deployments.
Listen on Spotify open.spotify.com →Includes a five-question checklist for spotting low-risk, high-impact agent use cases.
Open lennysnewsletter.com →Counters the "just wait for GPT-next" excuse; the engineering around the model decides success.
Open venturebeat.com →The next interaction pattern: agents that act on events, with humans approving at checkpoints.
Open inferencebysequoia.substack.com →Why agents win when scoped to one vertical job rather than general autonomy.
Open pigment.com →Notion rebuilt its agent architecture five times; hear what finally worked at 100M-user scale.
Open latent.space →