Domain 5: Context Management and Reliability (15%)

5.1 Managing Conversation Context to Preserve Critical Information

Key knowledge:

• Risks of progressive summarization: numeric values, percentages, and dates get condensed into vague summaries
• Lost-in-the-middle effect: models reliably process the start and end of long inputs, but may miss findings from the middle
• Tool outputs can accumulate in context disproportionately to relevance (40+ fields when 5 are needed)
• The importance of sending the full conversation history in subsequent API requests

Key skills:

• Extract transactional facts into a persistent “case facts” block outside the summarized history
• Trim verbose tool outputs down to relevant fields
• Place key findings at the beginning of aggregated data with explicit section headings
• Require subagents to include metadata (dates, sources) in structured outputs

5.2 Designing Effective Escalation Patterns and Resolving Ambiguity

Key knowledge:

• Suitable escalation triggers: explicit request for a human, policy gaps/exceptions, inability to make progress
• Immediate escalation (explicit request) vs attempt-to-resolve (within agent scope)
• Sentiment analysis and model confidence self-ratings are unreliable proxies for case complexity
• Multiple customer matches require asking for additional identifiers, not heuristic guessing

Key skills:

• Explicit escalation criteria with few-shot examples in the system prompt
• Execute explicit requests for a human immediately without additional investigation
• Escalate when policy is ambiguous or silent for a specific request
• Ask for additional identifiers when tool results contain multiple matches

5.3 Implementing Error Propagation Strategies in Multi-agent Systems

Key knowledge:

• Structured error context (failure type, query, partial results, alternatives) enables smarter coordinator recovery
• Distinguish access failures (timeouts require a retry decision) from valid empty results (no matches)
• Generic error statuses (“search unavailable”) hide valuable context from the coordinator
• Silent suppression or aborting the whole workflow on a single failure are both anti-patterns

Key skills:

• Return structured error context: failure type, what was attempted, partial results, possible alternatives
• Distinguish access failures from valid empty results
• Perform local recovery in subagents for transient failures; propagate only non-recoverable errors with partial results
• Annotate coverage in synthesis: what is well-supported vs where gaps remain

5.4 Managing Context Efficiently When Investigating Large Codebases

Key knowledge:

• Context degradation in long sessions: the model starts producing unstable answers and referring to “typical patterns” instead of specific classes
• Scratchpad files preserve key findings across context boundaries
• Delegating to subagents isolates verbose discovery output
• Structured state persistence enables crash recovery

Key skills:

• Spawn subagents for specific questions while keeping high-level coordination in the main agent
• Use scratchpad files to store key findings and reference them later
• Summarize key findings before spawning next-phase subagents
• Use /compact to reduce context usage during long investigations

5.5 Designing Workflows with Human Oversight and Confidence Calibration

Key knowledge:

• Aggregate metrics (e.g., 97% overall accuracy) can mask poor performance on specific document types or fields
• Stratified random sampling measures error rates in high-confidence extractions
• Field-level confidence calibration using labeled validation sets
• Validate accuracy by document type and field segment before automating

Key skills:

• Implement stratified random sampling to detect new error patterns
• Analyze accuracy by document type and field to validate stable performance
• Output field-level confidence scores and calibrate review thresholds using labeled data
• Route low-confidence or ambiguous-source extractions to human review

5.6 Preserving Provenance and Handling Uncertainty in Multi-source Synthesis

Key knowledge:

• Attribution is lost during summarization without preserving “claim → source” mappings
• Structured mappings must be preserved during aggregation
• Handle conflicting statistics by annotating conflicts with attribution rather than arbitrarily choosing one value
• Include publication/collection dates to avoid misreading temporal differences as contradictions

Key skills:

• Require subagents to output “claim → source” mappings (URL, document name, quotes)
• Structure reports to separate stable findings from disputed ones
• Preserve conflicting values with annotations and pass them to the coordinator for reconciliation
• Include publication dates for correct temporal interpretation
• Render content by type: financial data as tables, news as prose, technical findings as structured lists

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