Agent Designer

Agent Designer is a community skill for designing AI agent architectures from requirements, covering capability mapping, tool selection, memory design, safety constraints, and interaction pattern specification.

What Is This?

Overview

Agent Designer provides structured approaches for translating business requirements into AI agent architectural specifications. It covers capability analysis to determine what the agent needs to do, tool inventory selection, memory system design for context retention, safety boundary definition, user interaction pattern specification, and performance requirement documentation. The skill enables architects to produce agent designs that development teams can implement with clear specifications.

Who Should Use This

This skill serves solution architects designing agent systems for specific business use cases, product managers translating user needs into agent capability requirements, and technical leads evaluating feasibility of proposed agent features before development.

Why Use It?

Problems It Solves

Agent development without design specifications leads to capability gaps discovered late in implementation. Tool selection without systematic analysis results in either missing capabilities or unnecessary complexity. Memory system design is often an afterthought that causes agents to lose context or exceed token budgets. Safety constraints added retroactively are less effective than those designed into the architecture from the start.

Core Highlights

Capability mapping translates user stories into concrete agent abilities with measurable criteria. Tool inventory analysis matches required capabilities to available tools and identifies gaps. Memory architecture specification defines what context to retain, how long, and in what format. Safety boundary documentation establishes constraints that development teams implement as guardrails.

How to Use It?

Basic Usage

from dataclasses import dataclass, field

@dataclass
class Capability:
    name: str
    description: str
    required_tools: list[str] = field(default_factory=list)
    priority: str = "medium"

@dataclass
class AgentDesign:
    name: str
    purpose: str
    capabilities: list[Capability] = field(default_factory=list)
    safety_rules: list[str] = field(default_factory=list)
    memory_type: str = "conversation"
    max_context_tokens: int = 4096

    def add_capability(self, name: str, description: str,
                       tools: list[str], priority: str = "medium"):
        self.capabilities.append(Capability(
            name=name, description=description,
            required_tools=tools, priority=priority))

    def add_safety_rule(self, rule: str):
        self.safety_rules.append(rule)

    def get_required_tools(self) -> list[str]:
        tools = set()
        for cap in self.capabilities:
            tools.update(cap.required_tools)
        return sorted(tools)

    def validate(self) -> dict:
        issues = []
        if not self.capabilities:
            issues.append("No capabilities defined")
        if not self.safety_rules:
            issues.append("No safety rules defined")
        return {"valid": len(issues) == 0, "issues": issues}

Real-World Examples

from dataclasses import dataclass, field

@dataclass
class InteractionPattern:
    trigger: str
    agent_response: str
    requires_confirmation: bool = False

class AgentDesignReview:
    def __init__(self, design: AgentDesign):
        self.design = design
        self.patterns: list[InteractionPattern] = []

    def add_pattern(self, trigger: str, response: str,
                    confirm: bool = False):
        self.patterns.append(InteractionPattern(
            trigger=trigger, agent_response=response,
            requires_confirmation=confirm))

    def coverage_report(self) -> dict:
        covered = set()
        for pattern in self.patterns:
            for cap in self.design.capabilities:
                if cap.name.lower() in pattern.trigger.lower():
                    covered.add(cap.name)
        uncovered = [c.name for c in self.design.capabilities
                     if c.name not in covered]
        return {"covered": len(covered),
                "total": len(self.design.capabilities),
                "uncovered": uncovered}

    def export_spec(self) -> dict:
        return {
            "agent": self.design.name,
            "purpose": self.design.purpose,
            "tools": self.design.get_required_tools(),
            "safety_rules": self.design.safety_rules,
            "patterns": len(self.patterns)
        }

Advanced Tips

Map each capability to at least one test scenario that validates correct behavior during development. Design memory systems with explicit eviction policies that define what context gets dropped when limits are reached. Include failure mode documentation for each capability specifying how the agent should behave when tools are unavailable.

When to Use It?

Use Cases

Design a customer support agent specifying which tools, knowledge bases, and escalation paths it needs. Create an architecture document for a coding assistant that defines its capabilities, limitations, and safety boundaries. Evaluate whether a proposed agent feature set is feasible given available tools and model capabilities.

Related Topics

Agent architecture patterns, requirements engineering, system design documentation, capability modeling, and AI safety specification.

Important Notes

Requirements

Clear business requirements or user stories that define what the agent should accomplish. An inventory of available tools and APIs the agent can integrate with. Organizational policies that define safety boundaries for automated agent actions.

Usage Recommendations

Do: validate designs with stakeholders before implementation begins to catch requirement gaps early. Prioritize capabilities by business impact to guide incremental development. Include error handling specifications for every external tool the agent uses.

Don't: design agents with capabilities that exceed available tool and model limitations. Omit safety specifications assuming they can be added during implementation. Create monolithic designs when modular agent components would be easier to develop and test.

Limitations

Design specifications cannot predict all edge cases the agent will encounter in production. Capability requirements may change as users interact with early agent versions. Agent performance is ultimately bounded by the underlying model capabilities regardless of architectural quality.