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1394 lines (1394 loc) · 51.7 KB
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[
{
"reputAgentUrl": "https://reputagent.com/patterns/a2a-protocol-pattern",
"title": "A2A Protocol Pattern",
"category": "coordination",
"complexity": "complex",
"adoption": "emerging",
"bestFor": "Cross-vendor agent interoperability and standardized communication",
"problem": "Agents from different frameworks and vendors cannot reliably communicate or exchange capabilities, creating vendor lock-in and preventing cross-platform collaboration.",
"solution": "Implement standardized agent-to-agent communication using the A2A Protocol, where agents advertise capabilities via Agent Cards, communicate via HTTP/JSON-RPC, and follow standardized task lifecycle states.",
"considerations": "A2A is an emerging standard - monitor for protocol updates and ensure backward compatibility.",
"whenToUse": [
"Multi-vendor agent environments",
"Building agent marketplaces or platforms",
"Enterprise systems requiring interoperability",
"Cross-cloud agent orchestration"
],
"whenNotToUse": [
"Single-vendor, closed ecosystems",
"Simple single-agent applications",
"When proprietary protocols provide critical features"
],
"tradeoffs": {
"pros": [
"Vendor-agnostic interoperability",
"Standardized capability discovery",
"Enterprise-grade security built-in",
"Supported by 100+ companies"
],
"cons": [
"Implementation overhead for simple use cases",
"Standard still evolving",
"Requires infrastructure investment",
"May not support all proprietary features"
]
},
"evaluationDimensions": {
"safety": "High",
"accuracy": "High",
"cost": "Moderate",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"HTTP/JSON-RPC infrastructure",
"Agent Card schema",
"Authentication system"
]
},
"tags": [
"interoperability",
"protocol",
"standardization",
"enterprise",
"multi-agent"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/agent-registry-pattern",
"title": "Agent Registry Pattern",
"category": "discovery",
"complexity": "moderate",
"adoption": "common",
"bestFor": "Centralized or federated discovery of available agents and their capabilities",
"problem": "In multi-agent systems, agents need to find other agents to collaborate with. Without a registry, agents must be hardcoded or manually configured, limiting flexibility and scalability.",
"solution": "Implement a registry service where agents register their capabilities, endpoints, and metadata. Other agents query the registry to discover suitable collaborators dynamically.",
"considerations": "Implement proper TTL and health checking to avoid routing to dead agents. Consider caching for frequently-queried capabilities.",
"whenToUse": [
"Multi-agent platforms with dynamic agent pools",
"When agents join and leave frequently",
"Cross-team or cross-organization agent collaboration",
"Building agent marketplaces"
],
"whenNotToUse": [
"Small, static agent configurations",
"When all agents are known at design time",
"Tightly coupled agent pairs"
],
"tradeoffs": {
"pros": [
"Dynamic agent discovery",
"Decouples agent dependencies",
"Enables agent marketplaces",
"Supports health monitoring"
],
"cons": [
"Single point of failure (if centralized)",
"Registry must be highly available",
"Stale entries if agents crash",
"Query latency for discovery"
]
},
"evaluationDimensions": {
"safety": "Moderate",
"accuracy": "High",
"cost": "High",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Registry service",
"Agent metadata schema",
"Health checking"
]
},
"tags": [
"discovery",
"registry",
"service-discovery",
"catalog",
"metadata"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/agent-service-mesh-pattern",
"title": "Agent Service Mesh Pattern",
"category": "discovery",
"complexity": "complex",
"adoption": "specialized",
"bestFor": "Infrastructure-level agent discovery, routing, and observability",
"problem": "As agent systems scale, managing discovery, load balancing, security, and observability for agent-to-agent communication becomes complex. Each agent implementing these concerns creates duplication and inconsistency.",
"solution": "Deploy a service mesh layer that handles agent discovery, traffic routing, load balancing, security (mTLS), and observability transparently. Agents communicate through mesh proxies.",
"considerations": "Service mesh is powerful but complex. Start with simpler discovery patterns and adopt mesh when scale/compliance demands it.",
"whenToUse": [
"Large-scale production agent deployments",
"When security/compliance requires mTLS",
"Complex multi-environment deployments",
"When observability is critical"
],
"whenNotToUse": [
"Small agent deployments (< 10 agents)",
"Simple, direct agent communication",
"When infrastructure complexity is a concern",
"Resource-constrained environments"
],
"tradeoffs": {
"pros": [
"Transparent service discovery",
"Built-in security (mTLS)",
"Automatic load balancing",
"Rich observability (traces, metrics)"
],
"cons": [
"Significant infrastructure complexity",
"Latency overhead from proxies",
"Steep learning curve",
"Resource overhead"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "High",
"cost": "Low",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Kubernetes/container orchestration",
"Service mesh (Istio/Linkerd)",
"Ops expertise"
]
},
"tags": [
"discovery",
"service-mesh",
"infrastructure",
"kubernetes",
"observability",
"security"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/agentic-rag-pattern",
"title": "Agentic RAG Pattern",
"category": "orchestration",
"problem": "Traditional RAG retrieves documents once and generates responses, but complex questions require iterative retrieval, query refinement, and multi-hop reasoning.",
"solution": "Embed autonomous agents into the RAG pipeline that can dynamically plan retrieval strategies, evaluate results, and iteratively refine searches.",
"considerations": "Agentic RAG significantly increases latency and cost. Use for complex queries where traditional RAG falls short.",
"tags": [
"orchestration",
"rag",
"retrieval",
"multi-hop",
"adaptive"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/blackboard-pattern",
"title": "Blackboard Pattern",
"category": "coordination",
"complexity": "complex",
"adoption": "specialized",
"bestFor": "Asynchronous multi-agent collaboration on complex problems",
"problem": "Agents need to collaborate on complex problems but direct communication creates tight coupling and communication overhead.",
"solution": "Provide a shared knowledge repository (blackboard) where agents post findings and read updates, enabling asynchronous, loosely-coupled collaboration.",
"considerations": "Blackboard can become a bottleneck if too many agents read/write simultaneously. Consider partitioning for high-throughput systems.",
"whenToUse": [
"Research and investigation tasks",
"Multi-perspective analysis",
"Problems requiring diverse expertise",
"Scenarios where agents should work independently"
],
"whenNotToUse": [
"Simple sequential workflows",
"Real-time, synchronous requirements",
"Tasks with strict ordering dependencies"
],
"tradeoffs": {
"pros": [
"Loose coupling between agents",
"Agents can join/leave dynamically",
"Natural parallelism",
"Clear audit trail of contributions"
],
"cons": [
"Coordination overhead",
"Can become a bottleneck",
"Complex conflict resolution",
"Requires schema design"
]
},
"evaluationDimensions": {
"safety": "Moderate",
"accuracy": "High",
"cost": "Moderate",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Shared state infrastructure",
"Event system",
"Conflict resolution strategy"
]
},
"tags": [
"coordination",
"asynchronous",
"shared-state",
"collaboration",
"distributed"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/byzantine-consensus-pattern",
"title": "Byzantine-Resilient Consensus Pattern",
"category": "coordination",
"complexity": "complex",
"adoption": "specialized",
"bestFor": "Fault-tolerant agreement in adversarial or unreliable environments",
"problem": "In safety-critical domains, some agents may fail, hallucinate, or behave maliciously. Systems need to reach reliable agreement despite adversarial or faulty participants.",
"solution": "Implement Byzantine fault-tolerant consensus where agreement is reached even when up to 1/3 of agents are faulty. Use PBFT or modern variants with aggregated signatures for efficiency.",
"considerations": "BFT is expensive. Use only when Byzantine tolerance is truly required. Consider lighter alternatives for semi-trusted environments.",
"whenToUse": [
"Financial or healthcare agent systems",
"Multi-party agent collaborations (untrusted)",
"Mission-critical decision making",
"When agent reliability cannot be guaranteed"
],
"whenNotToUse": [
"Fully trusted agent environments",
"When latency is critical (BFT adds rounds)",
"Small-scale systems (overhead not justified)",
"When simple majority voting suffices"
],
"tradeoffs": {
"pros": [
"Tolerates malicious/faulty agents",
"Provable safety guarantees",
"Well-understood theory",
"Battle-tested in blockchain"
],
"cons": [
"High communication overhead (O(n²))",
"Requires 3f+1 agents to tolerate f failures",
"Complex to implement correctly",
"Adds significant latency"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "Very High",
"cost": "Very Low",
"latency": "Very Low"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Cryptographic signatures",
"Network protocol",
"Fault detection"
]
},
"tags": [
"byzantine",
"fault-tolerance",
"consensus",
"safety-critical",
"security"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/capability-attestation-pattern",
"title": "Capability Attestation Pattern",
"category": "discovery",
"complexity": "complex",
"adoption": "emerging",
"bestFor": "Verifying agent capabilities with proofs rather than trusting self-reported claims",
"problem": "Agents self-report their capabilities, but there is no verification. Malicious or poorly-built agents may claim capabilities they do not have, leading to task failures or security issues.",
"solution": "Implement capability attestation where agents must prove their capabilities through benchmarks, certifications, or cryptographic proofs. Verifiers validate claims before trusting agents.",
"considerations": "Attestation is only as good as the benchmarks. Invest in comprehensive, realistic evaluation suites that resist gaming.",
"whenToUse": [
"Multi-party agent ecosystems (untrusted agents)",
"High-stakes task delegation",
"Agent marketplaces with quality requirements",
"Compliance-driven environments"
],
"whenNotToUse": [
"Fully trusted, internal agent pools",
"Rapid prototyping (overhead not justified)",
"When self-reported capabilities are sufficient"
],
"tradeoffs": {
"pros": [
"Verified, trustworthy capabilities",
"Prevents capability fraud",
"Enables trust in unknown agents",
"Supports compliance requirements"
],
"cons": [
"Attestation overhead",
"Requires benchmark infrastructure",
"Capabilities may change over time",
"Complex to implement correctly"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "Very High",
"cost": "Low",
"latency": "Low"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Benchmark suite",
"Attestation service",
"Verification protocol"
]
},
"tags": [
"discovery",
"attestation",
"verification",
"trust",
"certification",
"benchmarks"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/capability-discovery-pattern",
"title": "Capability Discovery Pattern",
"category": "discovery",
"problem": "Agents cannot effectively collaborate if they don't know what other agents can do, leading to missed opportunities or inappropriate task delegation.",
"solution": "Implement standardized capability advertisement and discovery mechanisms, allowing agents to find and evaluate potential collaborators dynamically.",
"considerations": "Capability claims may be exaggerated or fraudulent. Implement verification challenges and reputation systems.",
"tags": [
"discovery",
"capabilities",
"a2a",
"negotiation",
"interoperability"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/chain-of-thought-pattern",
"title": "Chain of Thought Pattern",
"category": "orchestration",
"problem": "LLMs often make errors on complex reasoning tasks when asked to produce answers directly without showing their work.",
"solution": "Prompt agents to explicitly generate intermediate reasoning steps before reaching a conclusion, enabling verification and debugging of the thought process.",
"considerations": "CoT increases token usage and latency. For simple tasks, direct answers may be more efficient.",
"tags": [
"reasoning",
"prompting",
"transparency",
"debugging",
"accuracy"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/consensus-decision-pattern",
"title": "Consensus-Based Decision Pattern",
"category": "coordination",
"complexity": "moderate",
"adoption": "emerging",
"bestFor": "Multi-agent collective decision-making with deliberation or voting",
"problem": "Multi-agent systems need to make collective decisions, but single-agent decisions can be biased or incomplete. Direct voting can be brittle, and debate-based approaches do not scale well.",
"solution": "Implement structured consensus mechanisms where multiple agents independently generate solutions, then reach agreement through voting, deliberation, or hybrid approaches based on task type.",
"considerations": "Agent diversity is critical - agents with similar training will have correlated errors, reducing the benefit of consensus.",
"whenToUse": [
"High-stakes decisions requiring multiple perspectives",
"Tasks where individual agent errors are common",
"Situations requiring democratic or fair outcomes",
"Knowledge-intensive tasks (use deliberation)"
],
"whenNotToUse": [
"Time-critical, low-latency requirements",
"Simple factual queries with clear answers",
"When agent diversity is low (similar training/biases)"
],
"tradeoffs": {
"pros": [
"Reduces individual agent biases",
"Improves accuracy on complex tasks",
"13.2% improvement on reasoning tasks (voting)",
"Transparent decision-making process"
],
"cons": [
"Higher latency and cost (multiple agents)",
"Requires tie-breaking mechanisms",
"Can amplify shared biases",
"Coordination overhead"
]
},
"evaluationDimensions": {
"safety": "High",
"accuracy": "Very High",
"cost": "Low",
"latency": "Low"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Multiple diverse agents",
"Voting/consensus protocol",
"Tie-breaking strategy"
]
},
"tags": [
"consensus",
"voting",
"deliberation",
"multi-agent",
"decision-making"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/defense-in-depth-pattern",
"title": "Defense in Depth Pattern",
"category": "safety",
"complexity": "complex",
"adoption": "emerging",
"bestFor": "Production agent systems handling untrusted inputs with tool access",
"problem": "Single-layer defenses against prompt injection and malicious inputs are insufficient for agent systems with access to tools and data.",
"solution": "Implement multiple independent security layers so that failure of one layer does not compromise the entire system.",
"considerations": "Defense layers must be truly independent. A shared vulnerability defeats the purpose of layered defense.",
"whenToUse": [
"Agents with access to sensitive tools or data",
"Systems processing untrusted user input",
"Production deployments with security requirements",
"Multi-tenant agent platforms"
],
"whenNotToUse": [
"Internal tools with trusted users only",
"Prototype or demo systems",
"Systems without tool access or side effects"
],
"tradeoffs": {
"pros": [
"No single point of failure",
"Catches attacks that bypass individual layers",
"Provides defense-in-time (multiple chances to catch threats)",
"Meets security audit requirements"
],
"cons": [
"Significantly more complex to implement",
"Each layer adds latency",
"False positives multiply across layers",
"Requires ongoing maintenance"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "High",
"cost": "Low",
"latency": "Low"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Security expertise",
"Monitoring infrastructure",
"Incident response plan"
]
},
"tags": [
"safety",
"security",
"prompt-injection",
"defense",
"layered"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/dynamic-routing-pattern",
"title": "Dynamic Task Routing Pattern",
"category": "coordination",
"complexity": "moderate",
"adoption": "common",
"bestFor": "Intelligent task distribution based on real-time agent capabilities",
"problem": "Static task allocation wastes resources and creates bottlenecks. Systems need intelligent routing based on real-time agent capabilities, workload, and task characteristics.",
"solution": "Implement a routing layer that analyzes incoming tasks and dynamically routes them to the most appropriate agent based on capability matching, current load, historical performance, and cost.",
"considerations": "Monitor routing decisions for bias. Ensure new agents can be discovered and receive traffic.",
"whenToUse": [
"Heterogeneous agent pools with different specializations",
"Variable workload patterns",
"When optimizing for latency or cost",
"Systems requiring high availability"
],
"whenNotToUse": [
"Homogeneous agent pools",
"When all agents must see all tasks",
"Strictly ordered workflows"
],
"tradeoffs": {
"pros": [
"Optimal resource utilization",
"Automatic load balancing",
"Graceful degradation on failures",
"Can optimize for multiple objectives"
],
"cons": [
"Routing logic adds latency",
"Requires capability metadata",
"Can make debugging harder",
"Cold start for new agents"
]
},
"evaluationDimensions": {
"safety": "Moderate",
"accuracy": "High",
"cost": "High",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Agent capability registry",
"Load monitoring",
"Routing algorithm"
]
},
"tags": [
"routing",
"load-balancing",
"capability",
"dynamic",
"orchestration"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/emergence-monitoring-pattern",
"title": "Emergence-Aware Monitoring Pattern",
"category": "coordination",
"complexity": "moderate",
"adoption": "emerging",
"bestFor": "Detecting and adapting to emergent behaviors in multi-agent systems",
"problem": "Multi-agent systems exhibit emergent behaviors that were not explicitly programmed. Small changes in agent prompts or structure can create unpredictable cascading effects and unproductive loops.",
"solution": "Implement continuous monitoring for emergent behaviors including conversation loops, productivity degradation, and unexpected patterns. Trigger adaptive responses when anomalies are detected.",
"considerations": "Emergence monitoring is essential for production multi-agent systems. Start with basic loop detection and expand based on observed issues.",
"whenToUse": [
"Production multi-agent deployments",
"Systems with autonomous agent interactions",
"When reliability is critical",
"Long-running agent processes"
],
"whenNotToUse": [
"Single-agent systems",
"Short, bounded interactions",
"When full manual oversight is possible"
],
"tradeoffs": {
"pros": [
"Catches issues before they cascade",
"Enables adaptive self-healing",
"Provides operational visibility",
"Essential for production reliability"
],
"cons": [
"Monitoring overhead",
"Requires baseline establishment",
"False positives possible",
"Intervention logic can be complex"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "High",
"cost": "Moderate",
"latency": "High"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Logging infrastructure",
"Metrics pipeline",
"Alert system"
]
},
"tags": [
"monitoring",
"emergence",
"observability",
"reliability",
"self-healing"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/evaluation-driven-development-pattern",
"title": "Evaluation-Driven Development (EDDOps)",
"category": "evaluation",
"problem": "Traditional development separates building and testing phases, but LLM agents require continuous evaluation throughout their lifecycle.",
"solution": "Embed evaluation as a core driver of agent design, unifying offline (development-time) and online (runtime) evaluation in a closed feedback loop.",
"considerations": "Invest in evaluation infrastructure early. The cost of retrofitting evaluation is much higher than building it in from the start.",
"tags": [
"evaluation",
"development",
"lifecycle",
"monitoring",
"continuous"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/event-driven-agent-pattern",
"title": "Event-Driven Agent Pattern",
"category": "orchestration",
"problem": "Synchronous request-response patterns create tight coupling between agents and limit scalability for complex workflows.",
"solution": "Agents react to events broadcast by an event broker, enabling loose coupling, parallel processing, and resilient multi-agent systems.",
"considerations": "Event-driven systems add complexity. Ensure proper monitoring, dead-letter queues, and event schema management.",
"tags": [
"orchestration",
"event-driven",
"asynchronous",
"scalability",
"decoupled"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/guardrails-pattern",
"title": "Guardrails Pattern",
"category": "safety",
"complexity": "moderate",
"adoption": "common",
"bestFor": "Production agents requiring content safety and policy compliance",
"problem": "Agents can generate harmful, biased, or policy-violating outputs, and catching these issues after the fact is costly and dangerous.",
"solution": "Implement input and output guardrails that validate, filter, and constrain agent behavior in real-time, preventing harmful actions before they execute.",
"considerations": "Guardrails add latency and can create false positives. Balance protection level against user experience.",
"whenToUse": [
"Customer-facing agents",
"Regulated industries (healthcare, finance)",
"Systems processing user-generated content",
"Agents with tool or data access"
],
"whenNotToUse": [
"Internal development tools",
"Research prototypes with trusted users",
"When false positives are unacceptable"
],
"tradeoffs": {
"pros": [
"Catches issues before they reach users",
"Satisfies compliance requirements",
"Provides consistent policy enforcement",
"Can be updated independently of agents"
],
"cons": [
"Adds latency to every request",
"Can create false positives",
"Requires ongoing tuning",
"May block legitimate edge cases"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "Moderate",
"cost": "Moderate",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Policy definitions",
"Content classifiers",
"Logging infrastructure"
]
},
"tags": [
"safety",
"validation",
"filtering",
"compliance",
"security"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/handoff-pattern",
"title": "Handoff Pattern",
"category": "coordination",
"problem": "In multi-agent workflows, unclear transitions between agents cause context loss, duplicate work, and inconsistent user experiences.",
"solution": "Define explicit handoff protocols where agents formally transfer task ownership, context, and state to the next agent in the workflow.",
"considerations": "Context drift is the primary risk. Design explicit context schemas and validate at each handoff boundary.",
"tags": [
"coordination",
"workflow",
"context",
"transitions",
"sequential"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/hierarchical-multi-agent-pattern",
"title": "Hierarchical Multi-Agent Pattern",
"category": "orchestration",
"problem": "Single-level supervision cannot scale to complex enterprise workflows with dozens of specialized agents across multiple domains.",
"solution": "Structure agents into a multi-level hierarchy where higher-level supervisors coordinate domain-specific managers, who in turn direct specialized worker agents.",
"considerations": "Balance hierarchy depth against latency. Deep hierarchies provide more control but add communication overhead.",
"tags": [
"orchestration",
"enterprise",
"scalability",
"hierarchy",
"delegation"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/human-in-the-loop-pattern",
"title": "Human-in-the-Loop Pattern",
"category": "evaluation",
"complexity": "moderate",
"adoption": "common",
"bestFor": "High-stakes decisions requiring human oversight and approval",
"problem": "Fully autonomous agents make mistakes, take irreversible actions, or handle sensitive decisions without appropriate oversight.",
"solution": "Integrate human review at critical decision points, allowing approval, modification, or rejection of agent actions before execution.",
"considerations": "Balance HITL frequency against user friction. Too many interrupts cause fatigue; too few allow errors.",
"whenToUse": [
"Financial transactions above thresholds",
"Healthcare recommendations",
"Legal document generation",
"Any irreversible or high-impact actions"
],
"whenNotToUse": [
"High-volume, low-stakes operations",
"Real-time systems where latency is critical",
"Tasks where human review adds no value"
],
"tradeoffs": {
"pros": [
"Prevents costly mistakes",
"Builds user trust",
"Satisfies regulatory requirements",
"Captures edge cases for improvement"
],
"cons": [
"Adds latency to workflows",
"Creates bottlenecks at human review",
"Requires human availability",
"Can cause decision fatigue"
]
},
"evaluationDimensions": {
"safety": "Very High",
"accuracy": "Very High",
"cost": "Low",
"latency": "Very Low"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"Checkpoint system",
"Review queue UI",
"State persistence"
]
},
"tags": [
"evaluation",
"safety",
"oversight",
"approval",
"governance"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/llm-as-judge-pattern",
"title": "LLM-as-Judge Pattern",
"category": "evaluation",
"complexity": "simple",
"adoption": "common",
"bestFor": "Scalable quality assessment of agent outputs without human reviewers",
"problem": "Evaluating LLM agent outputs at scale is expensive with human reviewers, and traditional metrics cannot capture nuanced quality dimensions.",
"solution": "Use a separate LLM (the \"judge\") to evaluate agent outputs against defined criteria, providing scalable, consistent quality assessment.",
"considerations": "LLM judges exhibit their own biases. Use calibration data, multiple judges, and human spot-checks to ensure reliability.",
"whenToUse": [
"High-volume output evaluation",
"Consistent scoring across large datasets",
"Rapid iteration on agent quality",
"Regression testing and benchmarking"
],
"whenNotToUse": [
"Mission-critical decisions requiring human judgment",
"Highly subjective or creative evaluations",
"When judge model biases are not understood"
],
"tradeoffs": {
"pros": [
"Scalable to millions of evaluations",
"Consistent application of criteria",
"Much faster than human review",
"Can evaluate 24/7 without fatigue"
],
"cons": [
"Judges have their own biases",
"May miss nuanced quality issues",
"Requires calibration against human judgment",
"Can be gamed by adversarial outputs"
]
},
"evaluationDimensions": {
"safety": "Moderate",
"accuracy": "High",
"cost": "High",
"latency": "High"
},
"implementationComplexity": {
"timeEstimate": "hours",
"prerequisites": [
"Evaluation prompts",
"Calibration dataset"
]
},
"tags": [
"evaluation",
"quality",
"automated",
"llm",
"benchmarking"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/market-coordination-pattern",
"title": "Market-Based Coordination Pattern",
"category": "coordination",
"complexity": "complex",
"adoption": "specialized",
"bestFor": "Decentralized task allocation using auction and trading mechanisms",
"problem": "Centralized task allocation becomes a bottleneck at scale. Agents need decentralized mechanisms to bid for work based on capabilities and workload, enabling self-organizing systems.",
"solution": "Implement market-based coordination where tasks are auctioned and agents bid based on capability fit, current workload, and cost-effectiveness. Winners execute tasks and receive rewards.",
"considerations": "Careful mechanism design is required to prevent gaming. Consider using sealed-bid auctions for sensitive applications.",
"whenToUse": [
"Large-scale multi-agent deployments",
"Heterogeneous agent capabilities",
"Dynamic workload distribution",
"When optimizing for efficiency/cost"
],
"whenNotToUse": [
"Small, static agent pools",
"When fairness trumps efficiency",
"Tightly coupled workflows requiring synchronization",
"When agents cannot accurately estimate costs"
],
"tradeoffs": {
"pros": [
"Naturally load-balances across agents",
"Scales without central bottleneck",
"Self-organizing and adaptive",
"Incentive-aligned behavior"
],
"cons": [
"Complex to implement correctly",
"May lead to resource hoarding",
"Requires accurate capability/cost estimation",
"Can be gamed by strategic agents"
]
},
"evaluationDimensions": {
"safety": "Moderate",
"accuracy": "High",
"cost": "High",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "weeks",
"prerequisites": [
"Auction protocol",
"Agent capability registry",
"Payment/reward system"
]
},
"tags": [
"market",
"auction",
"trading",
"decentralized",
"coordination",
"economic"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/mcp-pattern",
"title": "Model Context Protocol (MCP) Pattern",
"category": "coordination",
"complexity": "moderate",
"adoption": "emerging",
"bestFor": "Standardized tool and context exchange between agents",
"problem": "Agents need standard ways to discover, request, and share tools and context across different systems. Ad-hoc integration creates fragility and vendor dependency.",
"solution": "Implement the Model Context Protocol for standardized tool discovery, resource exchange, and capability negotiation. MCP provides HTTP for agents - a universal protocol for secure context and tool sharing.",
"considerations": "MCP is becoming an industry standard. Early adoption positions you well for the emerging agent ecosystem.",
"whenToUse": [
"Building agent platforms or marketplaces",
"Integrating agents from multiple vendors",
"Sharing tools across agent boundaries",
"Enterprise agent infrastructure"
],
"whenNotToUse": [
"Single-agent applications",
"When proprietary integration is required",
"Simple, self-contained agents"
],
"tradeoffs": {
"pros": [
"Standardized tool integration",
"Works across frameworks",
"Security-first design",
"Growing ecosystem support"
],
"cons": [
"Protocol overhead for simple cases",
"Still evolving standard",
"Requires infrastructure investment"
]
},
"evaluationDimensions": {
"safety": "High",
"accuracy": "High",
"cost": "Moderate",
"latency": "Moderate"
},
"implementationComplexity": {
"timeEstimate": "days",
"prerequisites": [
"MCP client/server",
"Tool schemas",
"Authentication"
]
},
"tags": [
"protocol",
"tools",
"context",
"standardization",
"interoperability"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/mutual-verification-pattern",
"title": "Mutual Verification Pattern",
"category": "safety",
"problem": "In multi-agent systems, agents may propagate hallucinations or errors, creating false consensus through mutual reinforcement.",
"solution": "Implement cross-agent verification where agents independently evaluate each other's outputs before accepting them as valid.",
"considerations": "Verification adds latency and cost. Reserve full mutual verification for high-stakes decisions.",
"tags": [
"safety",
"verification",
"hallucination",
"consensus",
"multi-agent"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/orchestrator-worker-pattern",
"title": "Orchestrator-Worker Pattern",
"category": "orchestration",
"problem": "Complex tasks require parallel processing by specialized agents, but coordination overhead and context management become bottlenecks.",
"solution": "A lead orchestrator agent dynamically spawns and coordinates specialized worker subagents that operate in parallel, synthesizing their results into a coherent output.",
"considerations": "Emergent behaviors make debugging challenging. Implement robust logging and set clear boundaries for worker autonomy.",
"tags": [
"orchestration",
"parallel",
"workers",
"coordination",
"anthropic"
]
},
{
"reputAgentUrl": "https://reputagent.com/patterns/planning-pattern",
"title": "Planning Pattern",
"category": "orchestration",
"problem": "Complex tasks require structured approaches, but agents that dive directly into execution often miss dependencies or create suboptimal sequences.",