WORKFLOWS VS AGENTS: PICKING THE RIGHT PATTERN FOR PRODUCTION
Fuzzy Labs’ MLOps.WTF adopts Anthropic’s distinction: workflows follow predefined code paths, while agents choose their own next steps via autonomous loops. Use...
Fuzzy Labs’ MLOps.WTF adopts Anthropic’s distinction: workflows follow predefined code paths, while agents choose their own next steps via autonomous loops. Use workflows for well-defined, repeatable tasks; reserve agents for open-ended, multi-tool problems, and plan for step-level observability, debugging, and evaluation to manage non-determinism.
Choosing workflows for deterministic tasks reduces failure surface and simplifies monitoring versus agentic loops.
Agent loops require budgets, timeouts, and per-step tracing to control cost, latency, and reliability.
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terminal
A/B a workflow vs agent for the same task and track latency, success rate, tool-call count, and cost per request.
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terminal
Add step-level tracing and replay tests (inputs, tool calls, outputs) to reproduce failures across seeds/models.
Legacy codebase integration strategies...
- 01.
Introduce explicit workflow orchestration and logging around existing pipelines before gating any agent loop behind feature flags.
- 02.
Enforce quotas, timeouts, and circuit breakers on agentic paths with deterministic fallbacks to current rules/ETL or services.
Fresh architecture paradigms...
- 01.
Default to workflow-first design with clear tool interfaces, idempotent operations, and bounded retries/loop limits.
- 02.
Build observability early: per-step traces, metrics, and evaluation harnesses wired into CI.