Why most AI agents disappoint in production (and what to fix first)

AI agents that perform well in demos often fail in production due to four systemic weaknesses in the underlying data stack: stale data (freshness), inconsistent meaning across systems (semantics), unsafe write operations (reversibility), and lack of decision traceability (lineage). The post argues that agents amplify existing integration flaws because they operate continuously across systems. It recommends building explicit guarantees into the data substrate — freshness SLOs, semantic entity models, transactional write paths with plan-validate-commit patterns, and immutable audit trails — rather than patching issues at the agent layer. A practical readiness checklist advises starting with read-only advisory agents, proving retrieval quality, then gradually introducing scoped reversible writes.