Agents fail without defined handoff logicHandoff
Agents score predictions but miss hard triggers for handoff, causing ambiguous and often delayed transitions.
Operational risk: failures mount without metrics like time-to-handoff; escalations become untraceable.
Recommendation: embed decision points for handoff and audit loss across actual operational transitions.
Routing fails on exception
Routing is patched post hoc; exceptions hardcoded; routing rules start to conflict as new flows arrive.
A single misroute cascades into multi-process SLA breaches.
Recommendation: separate routing logic into a service zone, version all rules, push scenario simulation out of production.
In support, agents failed to distinguish escalation from informational queries; tickets stalled and SLA crashed. Solution: business signatures before queue assignment.
UI is not process infrastructureInterface
Agent rollouts default to repurposing leftover human UI. This breaks automation: the interface is not the business layer, and system actions belong in APIs.
Result: duplicate workload, race conditions, technical debt.
Recommendation: formalize APIs for every automation act. Human and system interfaces must never be collapsed.
LLMs generate noise without process anchoring
LLM output ignores process state and business restrictions. Content is ambiguous and post-processed by humans.
Output: a flood of irrelevant reports and manual corrections.
Recommendation: inject structured signals (KPIs, action lists), and log decision traces.
SaaS loses consistency at agent decision points
SaaS integrations expect static, synchronous flows. When agents drive action, status mismatches trigger failover and patchwork fixes.
Outcome: manual interventions, slowed transactions, unpredictable state transitions.
Recommendation: rebuild on event contracts; require full-cycle status loops and event-level idempotence.
AI agents do not reduce complexity, they just move the failure surface.
Automation fails where handoff boundaries go undefined.