Should I use a single AI agent or a multi-agent pipeline?

A single agent works best for straightforward tasks with 1-2 steps and limited complexity. Multi-agent pipelines are recommended when your workflow has 3+ distinct steps, requires different capabilities (e.g., data extraction + reasoning + content generation), involves 3+ external tool integrations, or demands high accuracy with quality-checking steps. This tool scores your use case across multiple criteria to make a recommendation.

How do I choose between Claude and OpenAI models?

Claude models (Anthropic) excel at nuanced reasoning, document analysis, and content generation with strong safety alignment. OpenAI models (GPT) offer strengths in structured output, code generation, and broad API ecosystem support. The choice also depends on pricing, latency requirements, and specific task characteristics. This advisor compares both providers for each step in your workflow.

What temperature should I use for AI agents?

Temperature depends on the task: use 0.0-0.2 for classification, data extraction, and structured output (consistency matters). Use 0.3-0.5 for analysis and summarization (balanced). Use 0.6-0.8 for content generation and creative tasks (variety matters). For high-stakes applications, reduce temperature by 0.1 from the default to increase determinism.

Free Tool

AI Agent Advisor

Use this tool to get a rough idea of what model architecture makes sense for your use case and which models would be the best fit. You can also get a ballpark estimate on costs. Works great with Nintex agents, Nintex Workflow Cloud, and other agentic AI platforms. It's designed as a starting point, not a finished answer.

Use Case

Discover your use case or browse presets

What are you receiving or processing?

Framework Questions

Complexity, volume, and stakes drive the recommendation

How much reasoning does this task require?

How often will this run?

~3,000 executions/month

What's the cost of getting it wrong?

How accurate does the response need to be?

Higher accuracy favors breaking work into focused sub-tasks with specialized agents

Task Characteristics

What does the agent need to do? Select all that apply.

Architecture

Workflow shape, dependencies, and format requirements

Workflow Steps2

110

2 distinct steps in the workflow

Steps depend on each other

Some steps can run in parallel

Human-in-the-loop review needed

Input Format

Output Format

Document & Tool Configuration

Avg Document Size per task

External Tools(APIs, databases, etc.)

Larger documents increase token usage. More tools add routing overhead and system prompt tokens.

Budget & Scale

Monthly budget, volume, latency, and provider preferences

Monthly AI Budget

Expected Monthly Volume(executions)

Latency Requirement

Provider Preference

Recommended Architecture

Multi-Agent Pipeline

Your use case scores 4/14 on our multi-agent criteria. multi-step workflow benefits from pipeline decomposition. dependent steps benefit from pipeline architecture. task mix spans Claude and OpenAI strengths — multi-agent can use each provider's best model.

Benefits

Specialized models per step reduce cost
Each agent optimized for its task
Easier to debug and monitor individual steps
Can mix Claude and OpenAI strengths

Considerations

More complex to implement and test
Orchestration logic adds latency
Need to handle inter-agent communication
Higher initial development cost

high confidence— Strong match between task characteristics and model capabilities

Step 1: Input Parser

Extract and structure data from raw inputs

Speed/Cost

ClaudeClaude 3 Haiku

$0.002/exec

Extremely fastVery low costReliable for simple tasks

Temp:0.1

Precision critical for structured data extraction

Also consider: GPT-4o Mini$0.15/0.6 per 1M tokens

Agent Pipeline

Input

Input Parser

Claude 3 Haiku

$0.0020/exec

Output

Input

Input Parser

Claude 3 Haiku

$0.0020

Output

Cost Estimation

Per Execution

$0.0020

Monthly

Annual

$70

Cost Range

Using cheapest models$2/mo

Recommended configuration$6/mo

Using most capable models$351/mo

Implementation Roadmap

Configure API credentials

Set up API keys for your selected model provider(s) and configure rate limits

Design prompt templates

Create and test prompt templates for each agent step with representative examples

Build orchestration logic

Implement the agent pipeline with input/output contracts between steps

Implement error handling

Add retry logic, fallback models, and graceful degradation for each agent

Test with representative data

Run 50-100 representative samples and measure accuracy, latency, and cost

Benchmark model alternatives

Compare your selected model against alternatives to validate the choice

Deploy to production

Deploy with feature flags for gradual rollout and easy rollback

Set up monitoring

Track accuracy, latency, cost per execution, and error rates in production

Schedule model reviews

Review model performance monthly — newer models may offer better cost/quality

Recommendations are based on general model capabilities and 2026 token pricing. Actual performance varies by use case, prompt design, and data characteristics. Always benchmark with your own data before production deployment. Model pricing is approximate and subject to change. This tool provides starting-point guidance — measure and adapt based on your results.

How the AI Agent Advisor Works

This free AI Agent Advisor helps you determine the right AI agent architecture and model selection for your use case. It applies a structured framework analyzing task complexity, volume, and stakes to recommend whether you need a single AI agent or a multi-agent pipeline, which specific models to use from Anthropic (Claude) and OpenAI (GPT) families, and what temperature and configuration settings to start with.

The 3-Question Framework

The advisor is built around three key dimensions that determine the right architecture:

Complexity — How many reasoning steps, data transformations, or decision points does your task involve? Low complexity (classification, simple extraction) can use fast economy models. High complexity (multi-step analysis, cross-referencing) needs more capable models.
Volume — How many times per day, week, or month will this run? High volume favors cost-efficient models and caching strategies. Low volume can afford premium models for better quality.
Stakes — What happens when the AI gets it wrong? Low-stakes tasks (content drafts, internal summaries) tolerate more errors. Critical tasks (legal compliance, financial decisions) need quality-checking agents and human review.

Single Agent vs. Multi-Agent Pipeline

The advisor scores your use case across multiple criteria to recommend an architecture:

Single agent — Best for straightforward tasks with 1-2 steps, limited tool usage, and moderate accuracy requirements. Simpler to build, deploy, and maintain. Lower cost per execution.
Multi-agent pipeline — Recommended when workflows have 3+ distinct steps, require diverse capabilities, use 3+ external tools, or demand high accuracy with verification steps. Each agent specializes in a subtask (parsing, analysis, generation, quality checking).

Models Compared

The advisor evaluates 16+ models across both providers:

Anthropic (Claude) — Claude 3 Haiku, Claude Haiku 4.5, Claude Sonnet 3.5/4.0/4.5, Claude Opus 4.0/4.5. Strong at nuanced reasoning, document analysis, instruction following, and safe content generation.
OpenAI (GPT) — GPT-4o-mini, GPT-4.1-mini, GPT-4.1-nano, GPT-4o, GPT-4.1, GPT-5, o1-mini, o3-mini, o4-mini. Strong at structured output, code generation, and broad tool ecosystem.

What You Get

Architecture recommendation (single or multi-agent) with scoring rationale
Per-step model selection with primary and alternative model suggestions
Temperature settings with task-specific rationale
Claude vs. OpenAI comparison for each pipeline step
Visual pipeline diagram showing the agent flow
Cost estimates per execution, monthly, and annual with budget constraint checking
Implementation checklist organized by phase (setup, development, testing, deployment)
Confidence indicator based on input completeness
Shareable URL to save and share your configuration

Use Case Presets

Start with 25+ presets across six categories: Document Processing (invoice processing, contract review, resume screening), Customer Service (ticket classification, escalation drafting, FAQ generation), Data Analysis (report generation, anomaly detection), Code & Technical (code review, documentation generation), Compliance & Legal (regulatory scanning, audit preparation), and Operations (approval routing, inventory monitoring). Each preset configures realistic defaults for that specific use case.