---
title: Software Agents: A Visual Guide to Capabilities, State, Tools, and Control Loops
canonical: https://hub.october.dev/software-agents-a-visual-guide-to-capabilities-state
description: Learn how software agents use capabilities, state, tools, and control loops, then see how October makes visual orchestration easier. Get started.
datePublished: 2026-09-06T07:15:06.503+00:00
dateModified: 2026-09-06T07:21:03.277374+00:00
---

# Software Agents: A Visual Guide to Capabilities, State, Tools, and Control Loops

Software agents are goal-directed systems that reason about a task, use tools, maintain relevant state, and act through an iterative control loop. In production, the key differentiator is the runtime around the model: visible capabilities, permissions, dependencies, handoffs, feedback, and stopping conditions.

## Software Agents: From Definition to Runtime Architecture

An agent combines a model with the runtime needed to pursue a goal. [IBM describes AI agents](https://www.ibm.com/think/topics/ai-agents) as systems that design workflows with available tools, make decisions, solve problems, interact with external environments, and take action.

That definition explains the outcome, but builders also need a model for inspecting the machinery behind it. A useful software-agent runtime exposes four boundaries:

1. **Capabilities:** what the system can reason about, plan, decompose, or adapt.
2. **State and context:** what must survive during execution and what intermediate results must be passed forward.
3. **Tools and actions:** which APIs, files, databases, search systems, or external services the agent can affect.
4. **Control loop:** how the system routes work, retries failures, delegates tasks, requests approval, and terminates.

The wrong default is to treat an agent as an LLM with a prompt or a chatbot wrapped around an automation script. An LLM generates responses. A deterministic workflow follows predefined steps. An agent selects actions, evaluates results, and iterates toward a goal under constraints.

October applies a spatial approach to this problem. Its visual AI agent orchestration environment is designed to give builders a clearer way to arrange agents, tools, workflows, and handoffs as a system they can inspect.

## The Four Primitives Every Agent Runtime Must Expose

A runtime becomes easier to operate when each primitive has observable evidence. The following matrix gives builders a practical inspection model.

| Primitive | Inspection question | Observable signals |
|---|---|---|
| Capabilities and reasoning | Can the system plan, decompose, or adapt? | Plans, task breakdowns, intermediate decisions |
| State and context | What must survive during the run or across sessions? | Execution context, intermediate results, session memory, retry state |
| Tools and actions | What can the agent query or change? | APIs, databases, web search, MCP, external systems |
| Control loop and orchestration | How does work proceed and stop? | Routing, handoffs, retries, approvals, termination |

When capabilities are hidden, teams cannot tell why an agent selected a particular path. Hidden state makes failures difficult to reproduce. Hidden tools create permission risk. Hidden control loops leave operators unsure whether a run has completed, stalled, or repeated the same failed action.

[OpenAI’s Agents SDK documentation](https://developers.openai.com/api/docs/guides/agents) describes agents that plan, call tools, collaborate across specialists, and maintain enough state for multi-step work. Its runner handles the tool loop, agent handoffs, and stopping or pausing conditions.

A coding task shows why these primitives matter. A planner decomposes a feature request, an implementation agent edits the repository, a test agent runs checks, and a human approval gate controls the merge or deployment step. The runtime records test results, routes failures back to implementation, and stops before a high-impact action requires approval.

[Jeff Su’s explanation of AI agents](https://www.youtube.com/watch?v=FwOTs4UxQS4) makes the distinction practical: an agent reasons, acts through tools, and iterates instead of producing one response and ending. That iterative loop is the difference between a generated answer and an executable system.

## Why Single-Agent Demos Become Multi-Agent Systems

One agent is often enough when a task has one dominant objective, a manageable toolset, and limited permissions. The design changes when the work contains separate modes such as research, planning, implementation, testing, critique, and deployment.

A feature request may require this sequence:

1. A coordinator interprets the request and breaks it into independently reviewable tasks.
2. Specialist agents operate within bounded contexts.
3. Reviewer agents inspect outputs against requirements.
4. The coordinator routes failures, retries selected steps, or requests human input.
5. An approval gate controls actions with production impact.

[AWS describes workflow orchestration agents](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/workflow-orchestration-agents.html) as systems that delegate work, maintain execution context, adapt to intermediate results, track state, retry failures, and pass results forward. AWS separately describes [multi-agent collaboration](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/multi-agent-collaboration.html) as a pattern in which agents with distinct roles or objectives share information and divide responsibility.

These patterns solve different coordination problems:

| Pattern | Coordination model | When it fits | Risk to inspect |
|---|---|---|---|
| Workflow orchestration | Central coordinator delegates through a structured flow | Sequencing, dependencies, retries, and state passing matter | Coordinator bottlenecks or single points of failure |
| Multi-agent collaboration | Specialized agents share information and divide responsibility | Work benefits from parallel perspectives or independent roles | Message consistency and coordination overhead |
| Single-agent execution | One agent owns the task and tool loop | The objective and permission boundary remain narrow | Overloaded context and weak separation of duties |

The operating rule is simple: add an agent when the task needs a different tool boundary, permission level, evaluation method, or context window. Do not add one for visual complexity alone. Each additional agent introduces coordination overhead, duplicated context, latency, token cost, and another recovery path.

Anthropic reports that its multi-agent research system outperformed a single-agent setup by **90.2% on an internal research evaluation** in [its account of the system](https://www.anthropic.com/engineering/multi-agent-research-system). That is a vendor-reported internal result, not an independent benchmark or a guarantee for coding workflows. Research tasks often contain parallelizable work, while many coding tasks require tightly coupled edits and review.

## How to Build a Spatial Agent Workflow in October

Build the workflow manually before choosing an orchestration environment. The process is:

1. Define the goal and the acceptable final artifact.
2. Break the goal into independently reviewable tasks.
3. Place each role in the workflow with a clear input and output.
4. Connect the tools and shared execution state each role needs.
5. Assign permissions according to the consequences of each action.
6. Add checkpoints before irreversible operations.
7. Set retry limits, failure routes, approval requirements, and termination conditions.
8. Run the workflow and inspect the execution path, intermediate results, and handoffs.

This manual method exposes the bottleneck: once agents and tools change frequently, a text list of prompts and integrations no longer shows how the system behaves as a whole. A visual workspace can make those relationships easier to arrange and inspect.

October is aimed at solo founders and small development teams managing fast-changing AI agent workflows. It provides a visual, spatial environment for orchestrating agents and runtime work, giving builders a way to see boundaries, dependencies, and handoffs while they evolve the system.

The alternatives serve different priorities. [Cursor Cloud Agents](http://cursor.com/docs/cloud-agent) run in isolated cloud virtual machines, support MCP servers, can build and test changed software, produce artifacts, support multi-repository environments, and run in parallel. Choose Cursor when focused AI-assisted coding is the central need.

[Langflow’s Agent documentation](https://docs.langflow.org/agents) covers multiple LLM providers, tool calling, custom instructions, and built-in chat memory in its version 1.11.x documentation. It suits visual flow construction and component-based experimentation, while component behavior can change between releases.

[AO’s official documentation](http://aoagents.dev/docs) describes isolated Git worktrees, branches, sessions, pull requests, CI, and review for parallel coding agents. It fits teams that want an agent-oriented software lifecycle with human review retained in the merge decision.

[Superset.sh](http://superset.sh/) describes an agent-agnostic workspace for orchestrating multiple coding agents in parallel, each in an isolated Git worktree. It appeals to teams seeking a different development surface for parallel coding work.

Choose visual orchestration when understanding boundaries, dependencies, permissions, and handoffs matters as much as generating code. For a short, fixed sequence with low-impact actions, deterministic automation remains easier to operate. Stop and quarantine any architecture claim that lacks primary documentation, any product ranking without a shared benchmark, or any product-specific implementation detail that public documentation does not establish.

For teams ready to turn that inspection model into a working spatial environment, October offers a focused path to visual agent orchestration and runtime design. [Try October Free - Get Started](https://october.dev/download).

## Frequently Asked Questions

### What are the 5 types of agent in AI?

[IBM identifies five conceptual types of agents](https://www.ibm.com/think/topics/ai-agent-types): simple reflex, model-based reflex, goal-based, utility-based, and learning agents. The taxonomy describes design behavior and adaptation, not product quality or a ranking of platforms.

### Is ChatGPT an agent or LLM?

[OpenAI characterizes ChatGPT](https://help.openai.com/en/articles/6783457-what-is-chatgpt) as a language model optimized for dialogue. The separate ChatGPT agent feature is described by OpenAI as a system that thinks, acts, and uses tools under the user’s guidance, so the answer depends on which product behavior is meant.

### What are top 5 AI agents?

There is no authoritative top-five ranking because these products serve different tasks and lack a shared apples-to-apples benchmark. Cursor, Langflow, AO Agents, Superset.sh, and October represent different runtime surfaces, from focused coding and visual flow construction to worktree-based orchestration and spatial agent design.

### What are some examples of agents?

Examples include a research agent that searches and synthesizes sources, a coding agent that edits and tests a repository, a coordinator that delegates tasks, and a review agent that checks another agent’s output. Complete implementations expose the tools, state, control loop, and approval conditions behind those actions, as shown in [OpenAI’s Agents SDK guide](https://developers.openai.com/api/docs/guides/agents) and [AWS’s workflow orchestration pattern](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/workflow-orchestration-agents.html).