Build agents, not pipelines · seangoedecke.com RSS feed
Science, Technology & Innovation · May 31, 2026
The author argues pipelines are not inherently safer than agents: both face identical risks from untrusted human input and action-triggering outputs, so safety should focus on sanitizing inputs and hard-limiting tool/action affordances (e.g., constrained email tools) and on permissioning and review paths rather than on architecture choice.
Build agents, not pipelines · seangoedecke.com RSS feed
Science, Technology & Innovation · May 31, 2026
The document argues that agentic systems are becoming the default because model progress disproportionately benefits systems that delegate decisions to LLMs—evidenced by successful coding agents—and therefore builders/investors should favor agent-first designs for higher optionality despite greater near-term uncertainty.
Build agents, not pipelines · seangoedecke.com RSS feed
Business, Finance & Industries · May 31, 2026
Pipelines win for large-scale production because bounded reasoning yields predictable, low-cost latency, while agentic loops introduce multiplicative runtime and cost risk—so separate cheap, bounded first-pass classification from expensive open-ended reasoning rather than exposing every request to unconstrained agents.
Build agents, not pipelines · seangoedecke.com RSS feed
Science, Technology & Innovation · May 31, 2026
The decisive difference between LLM pipelines and agents is control-flow ownership—pipelines use pre-authored code-defined flows while agents let the model manage flow—which only matters for multi-step, context-limited, or reactive tasks where agents enable adaptive, iterative actions and pipelines suffice for one-shot, fixed-step workflows.
Build agents, not pipelines · seangoedecke.com RSS feed
Science, Technology & Innovation · May 31, 2026
The key point is that pipelines fail because assembling the right context (what data the LLM actually gets) is often the bottleneck, and agentic systems that fetch missing information beat RAG-style retrieval because finding relevant information is as hard as solving the task and embeddings/cosine similarity often can’t do it.