Incomplete contexts: agent failures by system designSystem
Observation: Agents are trained on data but not mapped to operations—routing, ownership, and SLAs are absent.
Business consequence: Latency rises, manual escalations increase, and client SLAs are breached.
Operational fix: Bind agents to routing infrastructure with owner assignment, queues, and escalation logic. Use latency and queue length as core KPIs.
Agents lack human handoff logicOperation
Observation: Handoff flows are absent, often scattered at the interface instead of in orchestration.
Business consequence: Agents appear autonomous but unresolved escalations and manual intervention increase.
Operational fix: Insert explicit escalation triggers with SLA for queues; log each handoff and assign tiered ownership.
Most automation systems fail at the handoff layer.
Routing: automation breaks here
Observation: Routing uses LLM output, not deterministic rules or entity attributes.
Business consequence: Requests miss queues, triggering rework and repair cost.
Operational fix: Move routing to orchestrator with rule versioning, validation in delay, and fallback queues. Log rerouting metrics in SLAs.
Scenario: Return request routes to credit instead of logistics due to profile error. Fix: Use SKU+channel for routing and verify owner.
LLMs without context generate noiseData
Observation: Models hypothesize without business, temporal, or resource boundaries.
Business consequence: Operations act on invalid inferences; revenue lost through inefficient campaign.
Operational fix: Provide LLMs tight context—SLA, time, data source, owner. Limit tokens, add output checks.
Identity gaps: critical data loss
Observation: Agents miss cross-system identity—multiple IDs, missing source of truth.
Business consequence: Broken personalization, wrong contracts, lost sales.
Operational fix: Build identity resolution layer with rules, entity versions, and sync SLAs.
Testing ignores operational reality
Observation: Lab scenarios lack queues, collisions, and load peaking.
Business consequence: Passes CI, but fails in production under real pressure; triggers downtime and retro work.
Operational fix: Add latency, conflict, and data-failure to tests. Only then validate SLA flows.
System architecture: sensor/action lagArchitecture
Observation: Event streams out of sync lead to race conditions and faulty agent behaviors.
Business consequence: Actions based on outdated/partial data; operations revert and manually repair.
Operational fix: Standardize timing, implement event ordering, watermarking; define recovery domains and SLA.
LLM without routing logic is expensive autocomplete.
Checkpoints, ownership, SLAGovernance
Observation: No ownership map for agent decisions—accountability is undefined.
Business consequence: Disputes, budget overruns, and decision delays.
Operational fix: Attach contract SLAs to workflows, specify owners for events, log time and escalation events.
Routing: tactical checklistTactics
- Map inputs to queues via attribute rules.
- Push routing to orchestrator with rule versioning.
- Assign SLAs and timelines for each queue and escalation tier.
- Include fallback queues and backoff strategy on error.
- Log all handoffs with owner and timestamp.
Routing errors >1%/month are critical. Targets: L1→L2 escalation under 15 minutes, L2→L3 under 4 hours.
Operational summary: agent complexity is persistentSummary
Observation: Agents relocate, not reduce, complexity into operations.
Business consequence: Lacking orchestration, costs rise, predictability collapses.
Operational fix: Architect agents as business OS: routing, ownership, SLA, monitoring, checkpoints.