When Customer references specific amounts ('the 15% discount I mentioned') that were summarized away → Extract transactional facts (amounts, dates, order numbers) into a persistent 'case facts' block outside summarized history.

When Synthesis agent omits critical findings from the middle of 75K+ token aggregated input → Place a key findings summary at the beginning; organize the rest with explicit section headers.

When Tool outputs return 40+ fields per lookup when only 5 are relevant → Trim verbose tool outputs to only relevant fields before they accumulate in context.

When Policy is ambiguous or silent on the customer's specific request (e.g., competitor price matching) → Escalate to a human for policy interpretation -- do not fabricate a policy.

When get_customer returns multiple matches and the agent guesses wrong 15% of the time → Instruct the agent to ask for an additional identifier before taking any customer-specific action.

When The issue is straightforward but the customer explicitly asks for a human agent → Escalate immediately -- honor the explicit request without attempting to resolve first.

When A subagent encounters a timeout (transient failure) → Attempt local recovery; if it fails, propagate structured error context (failure type, what was attempted, partial results) to the coordinator.

When A subagent encounters a corrupted file (permanent failure) → Return the error with context to the coordinator -- do NOT retry (corruption is permanent).

When Some source categories succeed while others fail in a multi-source research task → Proceed with available data; annotate synthesis output with coverage gaps indicating which sources were unavailable.

When Discovery phase generates verbose output that fills the main context window → Use the Explore subagent or context: fork to isolate verbose output; return a concise summary.

When Extended exploration session shows signs of context degradation (vague references instead of specifics) → Have agents maintain scratchpad files recording key findings; use /compact to reduce context usage.

When Multi-phase task needs to persist findings across context boundaries → Summarize key findings from one phase before spawning sub-agents for the next; inject summaries into initial context.

When Overall accuracy is 97% but you suspect some document types perform poorly → Analyze accuracy by document type and field to identify hidden poor-performing segments.

When You want to reduce human review overhead on high-confidence extractions → Implement stratified random sampling of high-confidence outputs; only reduce review after validating by segment.

When Model outputs field-level confidence scores but they do not correlate with actual accuracy → Calibrate confidence thresholds using labeled validation sets rather than trusting raw model scores.

When Two credible sources report conflicting statistics on a key metric → Include both values with explicit source attribution; let the coordinator decide how to reconcile before synthesis.

When Subagent outputs are compressed and downstream agents lose track of which claims came from where → Require subagents to output structured claim-source mappings (source URLs, document names, excerpts).

When Data from different time periods appears contradictory → Require publication/collection dates in structured outputs to enable correct temporal interpretation.