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🎯
Intelligent Context Routing(ICR)
Dynamic routing of contextual information to the most appropriate processing components based on capability matching
Complexity: mediumPattern
Core Mechanism
Intelligent Context Routing decomposes incoming context (content, metadata, constraints) and matches each portion to the most appropriate processing component based on capability fit, policy constraints, and real-time signals. A router evaluates relevance, sensitivity, and utility, then routes minimal sufficient context to specialist components, preserving provenance and enabling safe aggregation of results.
🧭
Decompose
Split context by modality/topic/sensitivity
🎯
Match
Capability + policy aware routing
🛡️
Protect
Minimize/redact; comply by design
🔗
Aggregate
Provenance-aware merge of outputs
Workflow / Steps
- Ingest request + context; extract metadata (modality, sensitivity/PII, domain, language, SLA, budget).
- Decompose context into units (segments/chunks/signals) with labels and provenance.
- Consult capability registry (skills, models, tools, regions, compliance tags, cost/latency profiles).
- Score matches per unit: relevance × capability fit × policy/compliance × cost/latency.
- Route minimal necessary context to selected components; apply redaction and minimization.
- Execute components; enforce ordering/dependencies; cache reusable intermediates.
- Aggregate results with provenance; validate schema/quality; reconcile conflicts.
- Log features, decisions, and outcomes for audit, evaluation, and drift monitoring.
Best Practices
Use a typed context schema with sensitivity/PII flags, modality, provenance, and TTL.
Maintain an up-to-date capability registry with SLAs, cost, compliance regions, and evaluations.
Prefer routing via features/IDs over raw payloads; minimize and redact before dispatch.
Standardize I/O contracts for components to enable safe substitution and merging.
Budget-first routing: set token/time/cost caps per unit and choose cheapest viable path.
Calibrate router confidence and always define a safe default/fallback route.
Enable shadow routing in staging; compare decisions vs. oracle/evaluator before rollout.
Record rationale and evidence for each decision to support audit and improvement.
When NOT to Use
- Simple, single-component tasks where decomposition adds overhead without benefit.
- Very small systems lacking capability diversity or metadata to inform routing.
- Hard real-time micro-latency constraints that cannot afford routing/decomposition.
- Contexts with strict data residency constraints but no compliant target components.
Common Pitfalls
- Passing full payloads through the router causing privacy risk and cost bloat.
- Inconsistent schemas across components leading to fragile integrations.
- Over-decomposition causing excessive fan-out and token/latency spikes.
- Missing default/fallback path or escalation for low-confidence decisions.
- Stale capability registry or policy maps causing misroutes/non-compliance.
Key Features
Context decomposition with labels, sensitivity, and provenance
Capability- and policy-aware matching with confidence scoring
Redaction/minimization and least-privilege context delivery
Budget/SLA aware routing and backpressure
Provenance-preserving aggregation and validation
Decision logging, explainability, and audit trails
Drift and coverage monitoring with evaluators
Pluggable router policy: rules, classifiers, or lightweight LLM gate
KPIs / Success Metrics
- Routing accuracy vs. oracle/evaluator; misroute rate; coverage of required components.
- Latency overhead of routing/decomposition (P50/P95) and end-to-end impact.
- Token efficiency (quality per 1K tokens) and cost per task vs. monolithic baseline.
- Fallback/default frequency; override/escalation rate and time-to-recovery.
- Compliance incidents prevented; PII leakage rate; residency adherence.
- Cache hit ratios for reused context/intermediates; utilization balance of specialists.
Token / Resource Usage
- Total ≈ router features/tokens + per-component context + aggregation. Keep the router light.
- Route features/IDs instead of raw text where possible; pass only minimal necessary context.
- Use small models or rule engines for routing; reserve strong models for specialist processing.
- Cache decomposed units and intermediate results; deduplicate across components.
- Apply compression/summarization for sensitive or large segments before dispatch.
- Cap fan-out and parallelism; enforce per-run token/time/cost budgets.
Best Use Cases
- Multi-modal pipelines routing text/images/audio/video to specialized analyzers.
- Enterprise assistants combining product docs, tickets, logs, and policy with provenance.
- Multi-agent systems delegating context slices to domain specialists and evaluators.
- Compliance-sensitive workflows requiring data minimization and residency guarantees.
- Localization/translation flows where language/domain portions route to different components.
References & Further Reading
Academic Papers
- Enterprise Integration Patterns: Content-Based Router (Hohpe & Woolf, 2003)
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017)
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2021)
- Routing Transformer (Roy et al., 2021)
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance (2023)
Implementation Guides
- LangGraph decision routers and conditional edges
- LangChain RouterChain / MultiPromptRouter
- LlamaIndex RouterQueryEngine and selector components
- Kafka/NATS/RabbitMQ content-based routing patterns
- JSONLogic / JMESPath for rule evaluation; DSPy gating policies
Tools & Libraries
- LangGraph, LangChain, LlamaIndex routing components
- Kafka, RabbitMQ, NATS for message routing
- Feature stores/evaluators (MLflow, Evidently) for calibration
- Vector DBs and embedding services for similarity matching
Community & Discussions
- Enterprise Integration Patterns community resources
- LangChain/LangGraph and LlamaIndex community forums
- MLOps communities on evaluation, routing, and drift
🎯
Intelligent Context Routing(ICR)
Dynamic routing of contextual information to the most appropriate processing components based on capability matching
Complexity: mediumPattern
Core Mechanism
Intelligent Context Routing decomposes incoming context (content, metadata, constraints) and matches each portion to the most appropriate processing component based on capability fit, policy constraints, and real-time signals. A router evaluates relevance, sensitivity, and utility, then routes minimal sufficient context to specialist components, preserving provenance and enabling safe aggregation of results.
🧭
Decompose
Split context by modality/topic/sensitivity
🎯
Match
Capability + policy aware routing
🛡️
Protect
Minimize/redact; comply by design
🔗
Aggregate
Provenance-aware merge of outputs
Workflow / Steps
- Ingest request + context; extract metadata (modality, sensitivity/PII, domain, language, SLA, budget).
- Decompose context into units (segments/chunks/signals) with labels and provenance.
- Consult capability registry (skills, models, tools, regions, compliance tags, cost/latency profiles).
- Score matches per unit: relevance × capability fit × policy/compliance × cost/latency.
- Route minimal necessary context to selected components; apply redaction and minimization.
- Execute components; enforce ordering/dependencies; cache reusable intermediates.
- Aggregate results with provenance; validate schema/quality; reconcile conflicts.
- Log features, decisions, and outcomes for audit, evaluation, and drift monitoring.
Best Practices
Use a typed context schema with sensitivity/PII flags, modality, provenance, and TTL.
Maintain an up-to-date capability registry with SLAs, cost, compliance regions, and evaluations.
Prefer routing via features/IDs over raw payloads; minimize and redact before dispatch.
Standardize I/O contracts for components to enable safe substitution and merging.
Budget-first routing: set token/time/cost caps per unit and choose cheapest viable path.
Calibrate router confidence and always define a safe default/fallback route.
Enable shadow routing in staging; compare decisions vs. oracle/evaluator before rollout.
Record rationale and evidence for each decision to support audit and improvement.
When NOT to Use
- Simple, single-component tasks where decomposition adds overhead without benefit.
- Very small systems lacking capability diversity or metadata to inform routing.
- Hard real-time micro-latency constraints that cannot afford routing/decomposition.
- Contexts with strict data residency constraints but no compliant target components.
Common Pitfalls
- Passing full payloads through the router causing privacy risk and cost bloat.
- Inconsistent schemas across components leading to fragile integrations.
- Over-decomposition causing excessive fan-out and token/latency spikes.
- Missing default/fallback path or escalation for low-confidence decisions.
- Stale capability registry or policy maps causing misroutes/non-compliance.
Key Features
Context decomposition with labels, sensitivity, and provenance
Capability- and policy-aware matching with confidence scoring
Redaction/minimization and least-privilege context delivery
Budget/SLA aware routing and backpressure
Provenance-preserving aggregation and validation
Decision logging, explainability, and audit trails
Drift and coverage monitoring with evaluators
Pluggable router policy: rules, classifiers, or lightweight LLM gate
KPIs / Success Metrics
- Routing accuracy vs. oracle/evaluator; misroute rate; coverage of required components.
- Latency overhead of routing/decomposition (P50/P95) and end-to-end impact.
- Token efficiency (quality per 1K tokens) and cost per task vs. monolithic baseline.
- Fallback/default frequency; override/escalation rate and time-to-recovery.
- Compliance incidents prevented; PII leakage rate; residency adherence.
- Cache hit ratios for reused context/intermediates; utilization balance of specialists.
Token / Resource Usage
- Total ≈ router features/tokens + per-component context + aggregation. Keep the router light.
- Route features/IDs instead of raw text where possible; pass only minimal necessary context.
- Use small models or rule engines for routing; reserve strong models for specialist processing.
- Cache decomposed units and intermediate results; deduplicate across components.
- Apply compression/summarization for sensitive or large segments before dispatch.
- Cap fan-out and parallelism; enforce per-run token/time/cost budgets.
Best Use Cases
- Multi-modal pipelines routing text/images/audio/video to specialized analyzers.
- Enterprise assistants combining product docs, tickets, logs, and policy with provenance.
- Multi-agent systems delegating context slices to domain specialists and evaluators.
- Compliance-sensitive workflows requiring data minimization and residency guarantees.
- Localization/translation flows where language/domain portions route to different components.
References & Further Reading
Academic Papers
- Enterprise Integration Patterns: Content-Based Router (Hohpe & Woolf, 2003)
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017)
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2021)
- Routing Transformer (Roy et al., 2021)
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance (2023)
Implementation Guides
- LangGraph decision routers and conditional edges
- LangChain RouterChain / MultiPromptRouter
- LlamaIndex RouterQueryEngine and selector components
- Kafka/NATS/RabbitMQ content-based routing patterns
- JSONLogic / JMESPath for rule evaluation; DSPy gating policies
Tools & Libraries
- LangGraph, LangChain, LlamaIndex routing components
- Kafka, RabbitMQ, NATS for message routing
- Feature stores/evaluators (MLflow, Evidently) for calibration
- Vector DBs and embedding services for similarity matching
Community & Discussions
- Enterprise Integration Patterns community resources
- LangChain/LangGraph and LlamaIndex community forums
- MLOps communities on evaluation, routing, and drift