{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-asinghcsu--agenticrag-survey","slug":"asinghcsu--agenticrag-survey","name":"AgenticRAG-Survey","type":"agent","url":"https://arxiv.org/abs/2501.09136","page_url":"https://unfragile.ai/asinghcsu--agenticrag-survey","categories":["search"],"tags":["agentic","agentic-ai","agentic-framework","agentic-pattern","agentic-rag","agentic-workflow","llm-agent","multi-agent-systems","multiagent","rag","reflection","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-asinghcsu--agenticrag-survey__cap_0","uri":"capability://planning.reasoning.reflection.pattern.implementation.for.agent.self.evaluation","name":"reflection pattern implementation for agent self-evaluation","description":"Enables autonomous agents to evaluate their own outputs and decisions by implementing a feedback loop where agents assess correctness, identify errors, and determine areas for improvement. This pattern integrates introspection mechanisms that allow agents to critique their reasoning chains and trigger iterative refinement cycles without external intervention, forming the basis for self-correcting RAG pipelines.","intents":["I want my RAG agent to automatically detect when its retrieved context is insufficient and request additional retrieval","I need agents to validate their own answers against retrieved documents and flag inconsistencies","I want to implement self-correcting workflows where agents iteratively improve outputs based on internal evaluation"],"best_for":["Teams building autonomous RAG systems requiring high accuracy without human-in-the-loop validation","Developers implementing multi-turn agent workflows with quality gates","Researchers exploring self-improving LLM agent architectures"],"limitations":["Reflection adds computational overhead — requires additional LLM calls per evaluation cycle, typically 2-3x the base inference cost","Reflection quality depends on LLM capability — weaker models may fail to identify genuine errors in their reasoning","Risk of reflection loops becoming stuck in local optima without external guidance or hard stopping criteria"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)","Mechanism to track agent state and decision history across reflection cycles","Clear evaluation criteria or rubrics that agents can apply to their outputs"],"input_types":["agent output (text)","retrieved context (documents/text)","task specification (text)"],"output_types":["evaluation verdict (structured: pass/fail/needs-refinement)","critique explanation (text)","refinement instructions (text)"],"categories":["planning-reasoning","agent-self-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_1","uri":"capability://planning.reasoning.planning.pattern.for.multi.step.task.decomposition","name":"planning pattern for multi-step task decomposition","description":"Enables agents to create structured, hierarchical task plans by decomposing complex queries into sequential or parallel sub-tasks with explicit dependencies and execution order. The pattern uses LLM-based planning to generate task graphs that specify retrieval steps, reasoning stages, and tool invocations, allowing agents to orchestrate complex workflows autonomously rather than following fixed pipelines.","intents":["I want agents to break down complex research questions into retrieval, synthesis, and validation steps automatically","I need to generate execution plans that specify which documents to retrieve before performing analysis","I want agents to identify task dependencies and parallelize independent sub-tasks for efficiency"],"best_for":["Teams building complex question-answering systems requiring multi-stage reasoning","Developers implementing adaptive RAG where retrieval strategy depends on task complexity","Organizations needing explainable agent decision-making with visible task decomposition"],"limitations":["Planning overhead — generating detailed task plans adds 500ms-2s latency before execution begins","Plan quality varies with LLM capability — weaker models may generate suboptimal or redundant task sequences","No guarantee of plan feasibility — agents may plan tasks requiring unavailable tools or data sources"],"requires":["LLM capable of structured reasoning and task decomposition (GPT-4, Claude 3+)","Tool registry defining available retrieval methods, APIs, and processing capabilities","Task execution engine that can handle sequential and parallel task execution with dependency resolution"],"input_types":["user query (text)","task context (text)","available tools/capabilities (structured: JSON schema)"],"output_types":["task plan (structured: DAG or sequential list with dependencies)","task specifications (structured: tool name, parameters, expected outputs)","execution strategy (text: parallel vs sequential, resource requirements)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_10","uri":"capability://memory.knowledge.multi.agent.rag.architecture.with.specialized.retriever.and.generator.agents","name":"multi-agent rag architecture with specialized retriever and generator agents","description":"Implements a RAG system where distinct agents specialize in retrieval and generation, coordinating through shared context or message passing. The retriever agent focuses on finding relevant documents and evaluating retrieval quality, while the generator agent synthesizes responses from retrieved context. This separation enables specialization where each agent optimizes for its specific task while maintaining coordination through explicit communication protocols.","intents":["I want to separate retrieval expertise from generation expertise by using specialized agents","I need retrieval and generation to run in parallel or with explicit handoff protocols","I want to implement different evaluation criteria for retrieval quality vs generation quality"],"best_for":["Teams with domain expertise in both retrieval optimization and generation quality","Developers implementing systems where retrieval and generation have different SLAs or resource requirements","Organizations needing clear separation of concerns for maintainability and testing"],"limitations":["Coordination overhead — inter-agent communication and context synchronization adds latency and complexity","Context fragmentation — retriever and generator maintain separate context, risking inconsistencies or missed optimization opportunities","Debugging complexity — failures may occur in retrieval, generation, or coordination; harder to trace root causes than single-agent systems"],"requires":["Retriever agent with retrieval strategy and quality evaluation logic","Generator agent with synthesis and response generation logic","Coordination protocol (message passing, shared context store, or explicit handoff)","Context synchronization mechanism to ensure both agents have consistent information"],"input_types":["user query (text)","knowledge base or retrieval index (implicit: accessed by retriever)"],"output_types":["retrieved documents (structured: document list with relevance scores)","final response (text)","per-agent execution traces (structured: retriever decisions and generator decisions)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_11","uri":"capability://planning.reasoning.hierarchical.agentic.rag.with.multi.level.agent.organization","name":"hierarchical agentic rag with multi-level agent organization","description":"Organizes agents in a hierarchical structure where high-level agents handle task decomposition and coordination, mid-level agents manage specialized domains or processing stages, and low-level agents execute specific operations. Information flows up and down the hierarchy, with higher-level agents making strategic decisions and lower-level agents executing tactical operations. This enables scalable organization of complex reasoning across many agents with clear responsibility boundaries.","intents":["I want to organize agents in a hierarchy where senior agents delegate to junior agents","I need to implement multi-level reasoning where high-level strategy guides low-level execution","I want to scale agent systems by adding more agents at different hierarchy levels without restructuring"],"best_for":["Teams building large-scale enterprise RAG systems with complex organizational structures","Developers implementing systems where reasoning naturally decomposes into hierarchical levels","Organizations needing clear accountability where each hierarchy level has defined responsibilities"],"limitations":["Hierarchy rigidity — changing hierarchy structure requires reconfiguring agent relationships and communication protocols","Information loss — information flowing through multiple hierarchy levels may be summarized or filtered, losing detail","Latency accumulation — hierarchical decision-making adds latency as decisions propagate up and down the hierarchy"],"requires":["Hierarchical agent framework with parent-child relationships and delegation protocols","Clear role definitions for each hierarchy level","Information aggregation and summarization logic for upward information flow","Task decomposition logic for downward task delegation"],"input_types":["user query (text)","hierarchy structure (structured: agent relationships and role definitions)"],"output_types":["final response (text)","hierarchy execution trace (structured: per-level decisions and delegations)","responsibility mapping (structured: which agent handled which task)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_12","uri":"capability://memory.knowledge.corrective.agentic.rag.with.feedback.driven.iterative.refinement","name":"corrective agentic rag with feedback-driven iterative refinement","description":"Implements RAG systems with explicit feedback loops where agents detect retrieval or generation failures and trigger corrective actions. When agents identify that retrieved context is insufficient or generated responses are inaccurate, they autonomously adjust retrieval strategies (e.g., different query formulation, expanded search scope) or re-generate responses with corrected reasoning. This pattern enables self-correcting systems that improve output quality through iterative refinement driven by internal error detection.","intents":["I want agents to detect when retrieval failed to find relevant documents and retry with different strategies","I need systems that identify factual errors in generated responses and correct them automatically","I want to implement adaptive retrieval where agents adjust search strategy based on retrieval quality feedback"],"best_for":["Teams building high-reliability RAG systems where error correction is critical","Developers implementing systems that must handle diverse query types and knowledge domains","Organizations requiring systems that improve output quality through self-correction"],"limitations":["Correction overhead — detecting and correcting errors requires additional LLM calls, typically 2-3x base cost for corrected queries","Correction quality variability — error detection and correction quality depends on LLM capability; weaker models may miss errors or introduce new ones","Infinite loop risk — correction loops may not converge; requires hard stopping criteria to prevent infinite correction attempts"],"requires":["Error detection mechanism to identify retrieval or generation failures","Correction strategy library with alternative approaches for different failure types","Iteration control logic with maximum correction attempts and convergence detection","Quality metrics to evaluate whether corrections improved output"],"input_types":["user query (text)","initial retrieval results (structured: documents with relevance scores)","initial generated response (text)"],"output_types":["corrected response (text)","correction history (structured: detected errors and corrections applied)","quality improvement metrics (structured: before/after quality scores)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_13","uri":"capability://memory.knowledge.adaptive.agentic.rag.with.dynamic.strategy.selection.based.on.query.characteristics","name":"adaptive agentic rag with dynamic strategy selection based on query characteristics","description":"Implements RAG systems that dynamically adjust retrieval and generation strategies based on query analysis, task complexity, and available resources. Agents analyze incoming queries to determine optimal processing approach (e.g., simple retrieval vs multi-step reasoning, local vs remote execution) and select strategies that balance quality, latency, and cost. This pattern enables efficient resource utilization by matching processing complexity to query requirements rather than using uniform strategies for all queries.","intents":["I want to route simple factual queries to fast retrieval-only paths and complex reasoning queries to multi-step agent workflows","I need to adjust retrieval depth based on query complexity (simple queries need fewer documents)","I want to optimize for latency on simple queries and quality on complex queries"],"best_for":["Teams managing diverse query workloads with varying complexity and SLA requirements","Developers implementing cost-optimized systems where query complexity determines resource allocation","Organizations needing adaptive systems that balance quality, latency, and cost dynamically"],"limitations":["Strategy selection overhead — analyzing queries to determine optimal strategy adds latency before processing begins","Strategy mismatch risk — incorrect strategy selection may degrade quality or increase latency; requires monitoring and adjustment","Complexity in strategy definition — defining effective strategies for different query types requires domain expertise and tuning"],"requires":["Query analyzer that classifies queries by complexity, domain, and characteristics","Strategy registry with clear definitions of when each strategy is appropriate","Metrics to evaluate strategy effectiveness and trigger strategy adjustment","Monitoring to detect strategy mismatches and inform strategy refinement"],"input_types":["user query (text)","strategy definitions (structured: strategy_id → conditions and parameters)"],"output_types":["selected strategy (structured: strategy_id and parameters)","final response (text)","strategy effectiveness metrics (structured: quality, latency, cost)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_14","uri":"capability://memory.knowledge.graph.based.agentic.rag.with.knowledge.graph.integration.and.semantic.reasoning","name":"graph-based agentic rag with knowledge graph integration and semantic reasoning","description":"Implements RAG systems that leverage knowledge graphs to structure information and enable semantic reasoning across entities and relationships. Agents traverse knowledge graphs to find relevant information, reason about entity relationships, and synthesize responses based on graph structure. This pattern enables more sophisticated retrieval and reasoning by treating knowledge as interconnected entities and relationships rather than flat documents, supporting complex queries that require understanding of semantic relationships.","intents":["I want to retrieve information by traversing knowledge graph relationships rather than keyword matching","I need to answer questions that require understanding connections between entities (e.g., 'who are the competitors of companies founded by X')","I want to leverage semantic relationships in knowledge graphs to improve retrieval relevance"],"best_for":["Teams with structured knowledge domains (finance, healthcare, research) where entity relationships are critical","Developers implementing systems where semantic relationships improve answer quality","Organizations with existing knowledge graphs that can be leveraged for reasoning"],"limitations":["Knowledge graph dependency — system quality depends on knowledge graph coverage and accuracy; incomplete or outdated graphs degrade performance","Graph traversal complexity — finding relevant subgraphs for complex queries requires sophisticated traversal algorithms; may be computationally expensive","Knowledge graph maintenance burden — keeping knowledge graphs current requires ongoing curation and updates"],"requires":["Knowledge graph with entities, relationships, and properties","Graph traversal algorithms to find relevant subgraphs for queries","Entity linking to map query terms to knowledge graph entities","Semantic reasoning logic to synthesize responses from graph structure"],"input_types":["user query (text)","knowledge graph (structured: entities, relationships, properties)"],"output_types":["relevant subgraph (structured: entities and relationships relevant to query)","final response (text)","reasoning trace (structured: graph traversal path and semantic reasoning steps)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_15","uri":"capability://memory.knowledge.agentic.document.workflow.pattern.for.document.centric.processing.and.analysis","name":"agentic document workflow pattern for document-centric processing and analysis","description":"Implements specialized workflows for processing and analyzing documents where agents manage document ingestion, chunking, indexing, and multi-stage analysis. Agents coordinate document processing pipelines, apply domain-specific analysis (e.g., contract analysis, research paper summarization), and synthesize insights across documents. This pattern treats documents as first-class entities with explicit processing workflows, enabling sophisticated document analysis that goes beyond simple retrieval.","intents":["I want to implement document processing pipelines where agents coordinate ingestion, chunking, and indexing","I need to apply multi-stage analysis to documents (e.g., extract entities, classify, summarize)","I want to synthesize insights across multiple documents with explicit document-level reasoning"],"best_for":["Teams building document analysis systems (legal, research, compliance)","Developers implementing systems where document structure and metadata are important","Organizations processing large document collections requiring sophisticated analysis"],"limitations":["Document processing overhead — chunking, indexing, and analysis add significant latency before retrieval can begin","Chunking strategy impact — document quality depends on chunking strategy; poor chunking loses context or creates fragmented chunks","Metadata management complexity — tracking document metadata, versions, and processing status requires sophisticated state management"],"requires":["Document ingestion and parsing pipeline","Chunking strategy appropriate for document type","Document indexing and metadata storage","Multi-stage analysis agents for domain-specific processing","Document-level reasoning and synthesis logic"],"input_types":["documents (text, PDF, structured formats)","document processing configuration (structured: chunking strategy, analysis stages)"],"output_types":["processed documents (structured: chunks with metadata)","document analysis results (structured: per-document insights)","synthesized insights (text: cross-document analysis)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_2","uri":"capability://tool.use.integration.tool.use.pattern.with.schema.based.function.binding","name":"tool use pattern with schema-based function binding","description":"Enables agents to invoke external tools, APIs, and knowledge bases through a schema-based function registry that defines tool capabilities, parameters, and return types. Agents parse tool invocation requests from LLM outputs, validate parameters against schemas, execute tools with error handling, and integrate results back into the reasoning loop. This pattern supports both synchronous tool calls and asynchronous tool chains with result aggregation.","intents":["I want agents to call retrieval APIs, databases, and external services based on reasoning decisions","I need to define a catalog of available tools that agents can discover and invoke dynamically","I want agents to handle tool failures gracefully and retry with alternative tools or parameters"],"best_for":["Teams building agent systems that integrate with multiple external APIs and data sources","Developers implementing tool-augmented LLM applications with strict parameter validation","Organizations requiring auditable tool invocations with clear input/output logging"],"limitations":["Schema validation overhead — strict parameter checking adds latency and may reject valid but slightly malformed requests","Tool availability coupling — agents cannot gracefully degrade if tools are unavailable; requires fallback mechanism implementation","Context window pressure — tool schemas and execution results consume significant token budget, limiting context for reasoning"],"requires":["Tool registry with JSON Schema definitions for each available tool","LLM with function-calling capability (OpenAI, Anthropic, or compatible API)","Error handling framework for tool execution failures and timeout management","Logging/monitoring infrastructure to track tool invocations and results"],"input_types":["LLM output with tool invocation requests (text or structured)","tool schemas (JSON Schema format)","tool parameters (structured: JSON)"],"output_types":["tool execution results (structured or unstructured depending on tool)","error messages with retry guidance (text)","execution metadata (structured: latency, status, tool version)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_3","uri":"capability://planning.reasoning.multi.agent.collaboration.pattern.with.role.based.specialization","name":"multi-agent collaboration pattern with role-based specialization","description":"Enables multiple specialized agents to work together on complex tasks by assigning distinct roles (e.g., retriever, analyzer, synthesizer) and implementing coordination mechanisms for task delegation, result aggregation, and conflict resolution. Agents communicate through shared context or message-passing protocols, with a coordinator agent managing task distribution and ensuring outputs from specialized agents are integrated coherently into final responses.","intents":["I want to decompose RAG tasks across specialized agents (one for retrieval, one for analysis, one for synthesis)","I need agents to collaborate on multi-perspective analysis where different agents evaluate the same query from different angles","I want to implement hierarchical agent teams where senior agents delegate to junior agents and aggregate results"],"best_for":["Teams building enterprise RAG systems requiring specialized expertise (legal analysis, technical review, business impact)","Developers implementing multi-perspective reasoning where diverse viewpoints improve answer quality","Organizations needing transparent agent collaboration with clear role assignments and responsibility tracking"],"limitations":["Coordination overhead — managing multiple agent executions, message passing, and result aggregation adds 1-3s latency per collaboration cycle","Context fragmentation — each agent maintains partial context, requiring explicit synchronization mechanisms to prevent inconsistencies","Scaling challenges — coordination complexity grows quadratically with agent count; practical limit typically 3-5 agents per task"],"requires":["Multi-agent orchestration framework (e.g., LangGraph, AutoGen, or custom implementation)","Shared context store or message queue for inter-agent communication","Role definitions and specialization prompts for each agent type","Aggregation logic to combine outputs from multiple agents into coherent responses"],"input_types":["user query (text)","task specification with role requirements (structured)","agent capabilities and specializations (structured: role → capabilities mapping)"],"output_types":["per-agent outputs (structured: role → output mapping)","aggregated response (text)","collaboration trace (structured: agent interactions, decisions, and reasoning)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_4","uri":"capability://automation.workflow.prompt.chaining.workflow.pattern.for.sequential.task.execution","name":"prompt chaining workflow pattern for sequential task execution","description":"Structures complex tasks as sequences of dependent prompts where the output of one step becomes the input to the next, enabling step-by-step reasoning with explicit state transitions. Each step in the chain is a distinct LLM invocation with its own prompt, context, and validation logic, allowing agents to build up complex reasoning progressively while maintaining clear separation of concerns and enabling intermediate result inspection.","intents":["I want to break complex queries into sequential reasoning steps (retrieve → analyze → synthesize → validate)","I need to implement workflows where later steps depend on outputs from earlier steps","I want to enable human inspection and approval at intermediate steps in agent workflows"],"best_for":["Teams implementing transparent, auditable agent workflows where each step is independently verifiable","Developers building RAG systems with clear stage gates and quality checkpoints","Organizations requiring explainability where each reasoning step is documented and traceable"],"limitations":["Latency accumulation — sequential execution means total latency is sum of all steps; N steps = N × LLM latency, typically 2-5s per step","Context window management — each step consumes tokens for prompt + previous outputs; long chains risk exceeding context limits","Error propagation — errors in early steps cascade to later steps; requires explicit error handling and rollback mechanisms"],"requires":["LLM API with support for multiple sequential calls","State management to track outputs from each step and pass them to subsequent steps","Prompt templates for each step in the chain with clear input/output specifications","Error handling and validation logic between steps"],"input_types":["initial query (text)","step definitions (structured: sequence of prompts with input/output specs)"],"output_types":["per-step outputs (structured: step_id → output mapping)","final output (text)","execution trace (structured: step sequence with timings and validation results)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_5","uri":"capability://automation.workflow.routing.pattern.for.dynamic.task.direction.based.on.query.classification","name":"routing pattern for dynamic task direction based on query classification","description":"Classifies incoming queries or tasks and directs them to specialized processing pipelines or agents based on query type, complexity, or domain. The routing decision is made by an LLM-based classifier that analyzes query characteristics and selects the most appropriate handler from a registry of specialized processors, enabling efficient resource allocation and domain-specific optimization without requiring all queries to traverse the same pipeline.","intents":["I want to route simple factual queries to fast retrieval-only pipelines and complex reasoning queries to multi-step agent workflows","I need to direct domain-specific queries (legal, medical, technical) to specialized agents with domain knowledge","I want to implement cost optimization by routing high-volume simple queries to cheaper models and complex queries to stronger models"],"best_for":["Teams managing diverse query types with different processing requirements and SLAs","Developers implementing cost-optimized systems where query complexity determines model selection","Organizations with domain-specific expertise requiring specialized handlers for different query categories"],"limitations":["Classification latency — routing decision requires LLM inference, adding 200-500ms overhead before actual processing begins","Misclassification risk — incorrect routing sends queries to suboptimal handlers, degrading quality or increasing latency","Handler availability coupling — routing assumes all handlers are available; requires fallback routing if primary handler fails"],"requires":["Query classifier (LLM-based or ML model) with clear classification categories","Registry of specialized handlers/pipelines with clear input/output contracts","Fallback routing logic for misclassifications or handler unavailability","Metrics to monitor routing accuracy and handler performance by query type"],"input_types":["user query (text)","query classification schema (structured: category definitions and routing rules)"],"output_types":["routing decision (structured: selected handler/pipeline ID)","classification confidence (numeric: 0-1)","handler-specific output (varies by handler)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_6","uri":"capability://automation.workflow.parallelization.pattern.for.concurrent.task.execution.with.result.aggregation","name":"parallelization pattern for concurrent task execution with result aggregation","description":"Executes multiple independent tasks concurrently rather than sequentially, with explicit result aggregation and conflict resolution. The pattern identifies task independence, launches parallel executions, waits for all tasks to complete, and combines results using aggregation logic (e.g., voting, merging, ranking). This enables efficient utilization of computational resources and reduces total execution time for tasks with independent sub-components.","intents":["I want to retrieve from multiple knowledge sources in parallel and merge results","I need to run multiple analysis agents on the same query and aggregate their perspectives","I want to parallelize independent retrieval and reasoning tasks to reduce end-to-end latency"],"best_for":["Teams implementing high-throughput RAG systems where latency is critical","Developers building multi-perspective analysis systems that benefit from parallel evaluation","Organizations with access to multiple data sources or specialized agents that can be queried in parallel"],"limitations":["Resource contention — parallel execution increases concurrent API calls and memory usage; may hit rate limits or resource constraints","Result aggregation complexity — combining outputs from parallel tasks requires careful handling of conflicts, duplicates, and ranking","Debugging difficulty — parallel execution makes tracing errors and understanding failure modes more complex than sequential execution"],"requires":["Async/concurrent execution framework (Python asyncio, Node.js Promises, etc.)","Task dependency analysis to identify which tasks can run in parallel","Result aggregation logic specific to task type (e.g., deduplication for retrieval, voting for classification)","Resource management to prevent overwhelming downstream systems"],"input_types":["task list (structured: array of independent tasks with parameters)","aggregation strategy (structured: aggregation method and parameters)"],"output_types":["per-task results (structured: task_id → result mapping)","aggregated result (structured or text depending on aggregation method)","execution metadata (structured: per-task latency, success/failure status)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_7","uri":"capability://automation.workflow.orchestrator.workers.pattern.for.dynamic.task.delegation.and.coordination","name":"orchestrator-workers pattern for dynamic task delegation and coordination","description":"Implements a hierarchical coordination model where a central orchestrator agent analyzes tasks, decomposes them into sub-tasks, and delegates work to specialized worker agents. The orchestrator monitors worker progress, collects results, handles failures with retry logic, and synthesizes final outputs. Workers execute assigned tasks autonomously and report results back to the orchestrator, enabling scalable task distribution without requiring workers to understand the overall task structure.","intents":["I want a central coordinator to manage multiple worker agents and distribute tasks based on worker capabilities","I need to implement fault tolerance where the orchestrator reassigns failed tasks to alternative workers","I want to scale task processing by adding more workers without modifying the orchestrator logic"],"best_for":["Teams building large-scale multi-agent systems with heterogeneous worker capabilities","Developers implementing fault-tolerant systems where task reassignment is critical","Organizations needing dynamic scaling where workers can be added/removed without system reconfiguration"],"limitations":["Orchestrator bottleneck — central orchestrator becomes a single point of contention; scales to ~10-20 workers before coordination overhead dominates","Coordination overhead — orchestrator must maintain state for all in-flight tasks and workers, consuming memory and CPU","Failure detection latency — orchestrator must detect worker failures through timeouts or heartbeats, adding 5-30s detection delay"],"requires":["Orchestrator agent with task decomposition and worker management logic","Worker registry with capabilities and availability tracking","Task queue or message broker for task distribution and result collection","Failure detection and retry logic with exponential backoff","Monitoring/observability for orchestrator and worker health"],"input_types":["task specification (structured: task description, requirements, constraints)","worker registry (structured: worker_id → capabilities mapping)"],"output_types":["task assignments (structured: worker_id → assigned tasks)","final aggregated result (structured or text)","execution report (structured: per-worker results, failures, retries)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_8","uri":"capability://planning.reasoning.evaluator.optimizer.pattern.for.iterative.output.refinement","name":"evaluator-optimizer pattern for iterative output refinement","description":"Implements a feedback loop where an evaluator agent assesses outputs against quality criteria, identifies deficiencies, and an optimizer agent iteratively refines outputs based on evaluation feedback. The pattern cycles between evaluation and optimization until quality thresholds are met or iteration limits are reached. This enables continuous improvement of agent outputs without external intervention, with clear quality metrics driving the refinement process.","intents":["I want to automatically improve RAG outputs by iteratively refining them based on quality evaluation","I need to implement quality gates where outputs are rejected if they don't meet evaluation criteria","I want to optimize outputs for specific dimensions (accuracy, completeness, clarity) through targeted refinement"],"best_for":["Teams building high-quality RAG systems where output refinement is critical","Developers implementing self-improving agent systems with explicit quality metrics","Organizations requiring consistent output quality across diverse query types"],"limitations":["Iteration overhead — each refinement cycle requires evaluator + optimizer LLM calls, typically 2-4x the base inference cost","Convergence uncertainty — no guarantee that optimization will reach quality threshold; may require hard stopping criteria","Evaluation metric brittleness — quality criteria may not capture all dimensions of output quality; risk of optimizing for wrong metrics"],"requires":["Evaluator agent with clear quality criteria and scoring logic","Optimizer agent with refinement strategies for different quality deficiencies","Quality metrics (numeric or categorical) that drive refinement decisions","Iteration control logic with maximum iteration limits and convergence detection"],"input_types":["initial output (text)","evaluation criteria (structured: quality dimensions and thresholds)","context (text: original query, retrieved documents, etc.)"],"output_types":["evaluation scores (structured: per-dimension quality metrics)","refinement feedback (text: specific issues and improvement suggestions)","refined output (text)","iteration history (structured: per-iteration evaluations and refinements)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asinghcsu--agenticrag-survey__cap_9","uri":"capability://memory.knowledge.single.agent.rag.architecture.with.integrated.retrieval.and.generation","name":"single-agent rag architecture with integrated retrieval and generation","description":"Implements a unified RAG system where a single agent manages both retrieval and generation within a single reasoning loop. The agent decides when to retrieve, what to retrieve, evaluates retrieved context, and generates responses iteratively. This architecture integrates all agentic patterns (reflection, planning, tool use) into a single agent's decision-making process, enabling end-to-end control over the RAG pipeline without inter-agent coordination overhead.","intents":["I want a single agent that autonomously decides when and what to retrieve based on query analysis","I need an integrated system where retrieval and generation are tightly coupled in a single reasoning loop","I want to implement adaptive retrieval where the agent adjusts retrieval strategy based on intermediate results"],"best_for":["Teams building focused RAG systems for specific domains or query types","Developers implementing cost-optimized systems where single-agent simplicity reduces overhead","Organizations prioritizing simplicity and debuggability over multi-agent specialization"],"limitations":["Single point of failure — agent errors or hallucinations affect entire pipeline; no specialization to catch domain-specific issues","Context window pressure — single agent must maintain context for retrieval decisions, generation, and self-evaluation within limited token budget","Scaling limitations — single agent becomes bottleneck for high-throughput systems; difficult to parallelize work"],"requires":["LLM with strong reasoning and planning capabilities","Retrieval tool with clear invocation interface","Agent framework supporting iterative decision-making and tool use","Context management to track retrieval history and generation state"],"input_types":["user query (text)","knowledge base or retrieval index (implicit: accessed via tool)"],"output_types":["final response (text)","retrieval trace (structured: queries issued, documents retrieved)","reasoning trace (structured: agent decisions and evaluations)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)","Mechanism to track agent state and decision history across reflection cycles","Clear evaluation criteria or rubrics that agents can apply to their outputs","LLM capable of structured reasoning and task decomposition (GPT-4, Claude 3+)","Tool registry defining available retrieval methods, APIs, and processing capabilities","Task execution engine that can handle sequential and parallel task execution with dependency resolution","Retriever agent with retrieval strategy and quality evaluation logic","Generator agent with synthesis and response generation logic","Coordination protocol (message passing, shared context store, or explicit handoff)","Context synchronization mechanism to ensure both agents have consistent information"],"failure_modes":["Reflection adds computational overhead — requires additional LLM calls per evaluation cycle, typically 2-3x the base inference cost","Reflection quality depends on LLM capability — weaker models may fail to identify genuine errors in their reasoning","Risk of reflection loops becoming stuck in local optima without external guidance or hard stopping criteria","Planning overhead — generating detailed task plans adds 500ms-2s latency before execution begins","Plan quality varies with LLM capability — weaker models may generate suboptimal or redundant task sequences","No guarantee of plan feasibility — agents may plan tasks requiring unavailable tools or data sources","Coordination overhead — inter-agent communication and context synchronization adds latency and complexity","Context fragmentation — retriever and generator maintain separate context, risking inconsistencies or missed optimization opportunities","Debugging complexity — failures may occur in retrieval, generation, or coordination; harder to trace root causes than single-agent systems","Hierarchy rigidity — changing hierarchy structure requires reconfiguring agent relationships and communication protocols","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4748174965891763,"quality":0.25,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.35,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2025-10-20T00:30:31Z"},"community":{"stars":1594,"forks":178,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=asinghcsu--agenticrag-survey","compare_url":"https://unfragile.ai/compare?artifact=asinghcsu--agenticrag-survey"}},"signature":"XnDsxn3Srh2fhFBhIqH0WzCu3AX3HmMDmvzBrgFCP7KZe/wQqPSMpW4TyGBfhCfVgBGf1sieJnpLcq1+wOIYCQ==","signedAt":"2026-06-21T16:00:35.540Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/asinghcsu--agenticrag-survey","artifact":"https://unfragile.ai/asinghcsu--agenticrag-survey","verify":"https://unfragile.ai/api/v1/verify?slug=asinghcsu--agenticrag-survey","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}