Agentation

Agentation is a community skill for designing and building AI agent systems with structured architectures, covering agent lifecycle management, task decomposition, memory systems, tool integration, and multi-agent coordination patterns.

What Is This?

Overview

Agentation provides patterns for constructing AI agent systems that operate autonomously on complex tasks. It covers agent state machine design, task planning and decomposition, working memory management for context persistence, tool registry and execution frameworks, and inter-agent communication protocols. The skill enables developers to build reliable agent architectures that move beyond simple prompt chaining into structured autonomous workflows.

Who Should Use This

This skill serves developers building autonomous AI assistants that handle multi-step workflows, architects designing agent frameworks for team or organizational use, and engineers implementing production agent systems that need predictable behavior and error recovery capabilities.

Why Use It?

Problems It Solves

Simple prompt chains fail on complex tasks that require planning, branching, and error recovery. Agents without structured memory lose context across long task sequences. Tool integration without a registry pattern leads to duplicated code and inconsistent interfaces. Multi-agent systems without coordination protocols produce conflicting actions and wasted work.

Core Highlights

Agent state machines define clear transitions between planning, executing, and evaluating phases. Task decomposition breaks complex goals into manageable sub-tasks with dependency tracking. Memory systems persist relevant context across agent turns without exceeding token limits. Tool registries provide standardized interfaces for discovering and invoking external capabilities.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
from enum import Enum

class AgentState(Enum):
    IDLE = "idle"
    PLANNING = "planning"
    EXECUTING = "executing"
    EVALUATING = "evaluating"
    COMPLETE = "complete"

@dataclass
class Task:
    description: str
    status: str = "pending"
    result: str = ""
    dependencies: list[str] = field(default_factory=list)

class Agent:
    def __init__(self, name: str):
        self.name = name
        self.state = AgentState.IDLE
        self.tasks: list[Task] = []
        self.memory: list[dict] = []

    def plan(self, goal: str) -> list[Task]:
        self.state = AgentState.PLANNING
        tasks = [Task(description=f"Step for: {goal}")]
        self.tasks.extend(tasks)
        return tasks

    def execute_next(self, execute_fn) -> dict:
        self.state = AgentState.EXECUTING
        pending = [t for t in self.tasks if t.status == "pending"]
        if not pending:
            self.state = AgentState.COMPLETE
            return {"done": True}
        task = pending[0]
        result = execute_fn(task.description)
        task.status = "completed"
        task.result = result
        self.memory.append({"task": task.description,
                           "result": result})
        return {"done": False, "task": task.description}

Real-World Examples

from dataclasses import dataclass, field
from typing import Callable, Any

@dataclass
class Tool:
    name: str
    description: str
    handler: Callable = None

class AgentFramework:
    def __init__(self):
        self.agents: dict[str, Agent] = {}
        self.tools: dict[str, Tool] = {}

    def register_tool(self, tool: Tool):
        self.tools[tool.name] = tool

    def create_agent(self, name: str) -> Agent:
        agent = Agent(name=name)
        self.agents[name] = agent
        return agent

    def execute_tool(self, tool_name: str,
                     **kwargs) -> Any:
        tool = self.tools.get(tool_name)
        if not tool or not tool.handler:
            return {"error": f"Tool {tool_name} not found"}
        return tool.handler(**kwargs)

    def run_agent(self, agent_name: str,
                  goal: str, max_steps: int = 10) -> dict:
        agent = self.agents.get(agent_name)
        if not agent:
            return {"error": "Agent not found"}
        agent.plan(goal)
        steps = 0
        while steps < max_steps:
            result = agent.execute_next(
                lambda desc: f"Executed: {desc}")
            steps += 1
            if result.get("done"):
                break
        return {"agent": agent_name, "steps": steps,
                "completed": agent.state == AgentState.COMPLETE}

Advanced Tips

Implement a scratchpad memory pattern where the agent writes intermediate reasoning that persists across execution steps. Add circuit breakers that halt execution when consecutive failures exceed a threshold. Use structured output parsing for agent decisions to ensure deterministic state transitions.

When to Use It?

Use Cases

Build an autonomous research agent that plans search queries, reads documents, and synthesizes findings into a report. Create a code generation agent that decomposes features into implementation tasks and executes them sequentially. Design a customer service agent that navigates multi-step resolution workflows with tool access.

Related Topics

Agent architectures, task planning systems, ReAct prompting patterns, tool-using AI systems, and multi-agent coordination.

Important Notes

Requirements

A language model with tool-calling or function-calling capability. A tool registry with defined interfaces for external operations. State persistence mechanism for agent memory across execution turns.

Usage Recommendations

Do: define clear state transitions that prevent agents from getting stuck in loops. Limit maximum execution steps to prevent runaway agents from consuming resources. Log all agent decisions and tool calls for debugging and audit purposes.

Don't: allow agents to modify their own tool definitions or policy rules during execution. Build deeply nested agent hierarchies that become impossible to debug. Skip error handling for tool failures that leave tasks in an indeterminate state.

Limitations

Agent planning quality depends on the underlying language model reasoning capability. Complex task graphs with many dependencies increase the chance of cascading failures. Memory management becomes challenging for long-running agents that accumulate large context histories.