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name: agent-architecture-evaluator description: Use when evaluating, testing, and optimizing an agent architecture or multi-agent system. Best for reviewing planning, routing, memory, tool use, reliability, observability, cost, and system-level failure modes. version: "1.0.0"

Agent Architecture Evaluator

Version: 1.0.0

Overview

This skill reviews the architecture of an agent system, not just its prompts or its attached skills.

Use it for architectures involving components such as:

• planner / executor splits
• routers and specialists
• tool-use layers
• memory systems
• human approval gates
• multi-agent coordination

Use this skill when

• A user wants to assess an existing agent architecture.
• Reliability, latency, cost, or coordination problems appear to be architectural.
• A team needs a structured architecture review and optimization roadmap.
• You need system-level test scenarios rather than single-skill evals.

Do not use this skill when

• The problem is one isolated skill.
• The task is to create a new skill from scratch.
• The main need is portfolio review across many related skills.

Use agent-test-measure-refine or agent-skill-portfolio-evaluator in those cases.

Output contract

Always produce these named outputs:

• architecture_inventory
• failure_mode_map
• architecture_test_plan
• optimization_roadmap
• measurement_plan
• architecture_recommendation

Review dimensions

Evaluate at least these dimensions:

1. component clarity
2. routing correctness
3. memory usefulness
4. coordination reliability
5. cost and latency efficiency
6. observability and debuggability

Quick start

1. Map the current architecture.
2. Identify critical paths and failure-prone handoffs.
3. Define architecture-level test scenarios.
4. Identify bottlenecks in routing, memory, tools, or coordination.
5. Recommend the smallest structural changes with the highest leverage.

Workflow

1. Build the architecture inventory

Capture:

• components
• responsibilities
• inputs and outputs
• state or memory boundaries
• human approval points
• observability signals

2. Map failure modes

Look for:

• planner produces unusable tasks
• router sends work to the wrong specialist
• memory pollutes current decisions
• tool calls are slow, redundant, or poorly validated
• multi-agent handoffs lose context
• approval gates appear too late

3. Design system tests

Cover:

• happy path
• degraded upstream input
• partial component failure
• tool unavailability
• stale or noisy memory
• high-latency coordination
• rollback or recovery behavior

See references/architecture-review-framework-v1.0.0.md.

4. Prioritize architectural changes

Prefer:

• clarifying responsibilities before adding components
• removing weak indirection
• tightening interface contracts
• adding observability before adding complexity
• isolating state when cross-contamination is likely

5. Define measurement

Recommend concrete metrics where available:

• task success rate
• retry rate
• fallback rate
• cost per successful task
• latency by stage
• human intervention rate

Anti-patterns

• adding new components to hide unclear ownership
• keeping weak memory because it sounds sophisticated
• optimizing one stage without measuring system impact
• blaming prompts for structural routing failures

Resources

• references/architecture-review-framework-v1.0.0.md for system review steps.
• references/optimization-patterns-v1.0.0.md for architecture optimization guidance.
• assets/architecture-review-template.md for the final report structure.
• assets/example-architecture-review.md for a realistic filled review.
• assets/architecture-input-example.json for structured input.
• scripts/render_architecture_review.py to normalize a structured architecture review into Markdown.