Agent Evaluation Patterns

Discovering vulnerabilities, edge cases, and failure modes before production deployment

Verifying agent capabilities with proofs rather than trusting self-reported claims

Infrastructure-level agent discovery, routing, and observability

Finding agents by natural language description rather than exact capability tags

Centralized or federated discovery of available agents and their capabilities

Detecting and adapting to emergent behaviors in multi-agent systems

Fault-tolerant agreement in adversarial or unreliable environments

Standardized tool and context exchange between agents

Intelligent task distribution based on real-time agent capabilities

Complex tasks requiring context isolation and recursive decomposition

Decentralized task allocation using auction and trading mechanisms

Multi-agent collective decision-making with deliberation or voting

Cross-vendor agent interoperability and standardized communication

Production agents requiring content safety and policy compliance

Generic agents lack the specialized expertise needed for complex domain tasks, and coordinating multiple generic agents creates confusion about respon...

Complex tasks require parallel processing by specialized agents, but coordination overhead and context management become bottlenecks....

Linear chain-of-thought reasoning commits to a single path and cannot backtrack when that path leads to a dead end....

LLMs often make errors on complex reasoning tasks when asked to produce answers directly without showing their work....

Synchronous request-response patterns create tight coupling between agents and limit scalability for complex workflows....

Traditional RAG retrieves documents once and generates responses, but complex questions require iterative retrieval, query refinement, and multi-hop r...

LLMs have limited ability to perform calculations, access real-time data, or interact with external systems through language alone....

Complex tasks require structured approaches, but agents that dive directly into execution often miss dependencies or create suboptimal sequences....

Improving output quality through iterative self-critique

Adaptive, tool-using agents that need to respond to dynamic situations

In multi-agent systems, agents may propagate hallucinations or errors, creating false consensus through mutual reinforcement....

Production agent systems handling untrusted inputs with tool access

Traditional development separates building and testing phases, but LLM agents require continuous evaluation throughout their lifecycle....

Agents cannot effectively collaborate if they don't know what other agents can do, leading to missed opportunities or inappropriate task delegation....

High-stakes decisions requiring human oversight and approval

Scalable quality assessment of agent outputs without human reviewers

In multi-agent workflows, unclear transitions between agents cause context loss, duplicate work, and inconsistent user experiences....

Asynchronous multi-agent collaboration on complex problems

Single-level supervision cannot scale to complex enterprise workflows with dozens of specialized agents across multiple domains....

Multi-agent workflows requiring clear coordination and audit trails