AI Explained: A Guide to Understanding AI Agents

You've probably heard the term "AI agent" thrown around a lot lately. It's become the hot buzzword in tech circles, promised to revolutionize everything from customer service to personal productivity. But what exactly is an AI agent, and why should you care?

Here's the thing: "AI agent" has become a catch-all term that covers everything from simple chatbots to sophisticated autonomous systems. And that's actually okay! The key is understanding where different AI tools fall on the spectrum of agentic AI – because not all agents are created equal, and knowing the difference will help you set the right expectations and choose the right tools for your needs.

What Is an AI Agent? Understanding the Spectrum of Agentic AI Autonomy

The term agent has become something of a hot topic in the AI world. Some believe it should be reserved strictly for autonomous systems—AI that can perceive, decide, and act independently over time. Others use it far more broadly, applying the label to everything from copilots and chatbots to smart search tools. The truth is, both camps have a point. That’s why it’s helpful to step back and define what we really mean when we talk about “AI agents”. How much agency does each type of AI system truly have?

At its core, at least linguistically, an agent is simply something that acts on behalf of something or someone else. By that definition, nearly all AI systems are agents of some kind: they exist to perform a task, solve a problem, or drive an outcome. Whether they do this reactively or proactively, manually or autonomously, all AI can be understood as agents with a purpose.

But not all agents are created equal. Agency is a spectrum; As you move along the spectrum, AI systems shift from simple responders to systems that reason, adapt, and act with increasing autonomy.

Building on frameworks developed by leading AI researchers, we can map out today’s evolving landscape of AI agency. Here's how the modern agentic AI spectrum breaks down:

Low Agentic AI (Traditional AI Apps):

• Reactive: You ask a question, they give an answer

• Single-task focused: They perform one function at a time

• Require human input for each action

• Don't maintain context between sessions

• Examples: simple chatbots, AI writing tools

Mid-Level Agentic AI (Tool-Calling Agents):

• Can choose which tools to use based on the situation

• Handle multi-step tasks with some guidance

• Make decisions within defined parameters

• Maintain some context and memory

• Examples: AI assistants that can research and compile reports, workflow automation tools

High Agentic AI (Multi-Step Agents):

• Run in loops, continuously taking actions until a goal is achieved

• Can determine when to continue working or when they're done

• Handle complex, multi-step workflows independently

• Learn from each step and adjust their approach

• Examples: Advanced research agents, autonomous customer service systems

Autonomous Economic AI (Independent Agents):

• Can manage their own resources and make financial decisions

• Interact with blockchain systems and DeFi protocols

• May hold and manage digital assets or cryptocurrency

• Operate with minimal human oversight for extended periods

• Examples: Autonomous trading bots, AI-managed investment funds, self-sustaining digital businesses

Decentralized Autonomous AI (DeAI Systems):

• Operate across distributed networks without central control

• Make collective decisions through consensus mechanisms

• Exist on blockchain infrastructure with immutable logic

• Cannot be controlled or shut down by any single entity

• Examples: Decentralized prediction markets, autonomous DAOs, distributed AI networks

The key insight is that all of these are "agents", they just operate at different levels of autonomy. As you move up the spectrum, the agents become more capable of independent action and decision-making, giving you more powerful automation but requiring more trust in the AI's decision-making abilities. The newest levels – autonomous economic agents and decentralized AI – represent the frontier where AI systems begin to operate with true independence, potentially reshaping how we think about ownership, control, and economic participation in the digital age.

How AI Agents Are Created

Depending on where an agent lies on the agentic spectrum, its design and technical complexity will vary. From simple chatbots to decentralized autonomous systems, every AI agent combines a specific set of components that determine how much agency it possesses.

At the low end, agents are reactive tools guided entirely by human input. At the high end, they operate independently, manage resources, and make decisions over time with minimal (or no) human intervention.

Here’s a breakdown of how agents are built at each level of autonomy:

Building Low Agentic AI: Traditional AI Apps & Reactive Tools

These agents are simple and stateless. They don’t make decisions beyond generating a response to direct input.

Key Components:

• Pre-trained LLM (or task-specific ML model): Used in a stateless, reactive mode (e.g., GPT-4, Claude)
  
  

• Frontend Interface: A web/app UI for user interaction
  
  

• System Prompt: Carefully crafted instructions that shape the model’s responses
  
  

• No Memory: No awareness of past interactions or ongoing state
  
  

• No Tool Use: Only text in, text out
  
  

• No Planning or Decision-Making

Tech Stack:
LLM API (e.g. OpenAI) + Prompt + UI. Runs entirely through API calls.

Use Cases:
Static chatbots, basic Q&A assistants, AI writing tools

Building Mid-Level Agentic AI: Tool-Calling & Task-Oriented Agents

These agents go beyond static responses. They can choose tools, complete multi-step tasks, and maintain session-level context. They're ideal for use cases where you need a smarter assistant that can act across multiple systems.

Key Components:

• LLM Core: Interprets inputs, provides reasoning, and generates outputs
  
  

• Tool Integration: Connects to external services like APIs, databases, calculators, or search engines
  
  

• Orchestration Layer (e.g. LangChain, LlamaIndex): Manages how the agent chains together tools, memory, and language output; routes actions based on intent
  
  

• Short-Term Memory: Tracks the current session’s context and prior steps for more coherent responses
  
  

• State Manager: Monitors task progress, tool outputs, and current goals to keep the agent on track
  
  

• Bounded Autonomy: Executes within predefined constraints (no self-initiated loops or persistent goals)

Tech Stack:
OpenAI or Anthropic LLM + LangChain / LlamaIndex for orchestration + Vector Database (e.g., Pinecone, Weaviate) + Tool APIs

Use Cases:
AI copilots, contextual research assistants, workflow helpers, multi-tool support bots

Building High Agentic AI: Autonomous Multi-Step Agents

These agents can pursue goals over time, adjust their strategy based on feedback, and handle complex, multi-step tasks with little to no user input.

Key Components:

• LLM Core + Planner: The LLM interprets the goal; a planning module breaks it into actionable sub-tasks (e.g., using ReAct, Tree-of-Thought, or planner APIs).
  
  

• Autonomous Loop Runner: Executes an iterative cycle of plan → act → evaluate until success or termination
  
  

• Tool Integration: Expanded toolset including web browsers, code interpreters, vector search, and API connectors
  
  

• Long-Term Memory: Stores task history, learned behaviors, and strategic adjustments across sessions
  
  

• State & Feedback Manager: Tracks what’s been done, interprets tool responses, and adapts the workflow accordingly
  
  

• Safety & Guardrails: Hard-coded or learned constraints to prevent unwanted behavior or infinite loops

Tech Stack:

• Orchestration: LangGraph (for graph-based state tracking), AutoGen (multi-agent coordination), CrewAI (structured task teams)
  
  

• Memory: Vector databases (e.g., Chroma, Weaviate), JSON storage, or custom memory modules
  
  

• Execution Environment: Local runtimes, cloud workers, or sandboxed environments (e.g., Docker + async task queues)

Use Cases:

• Fully autonomous research agents
  
  

• Code generation bots that debug and test their own output
  
  

• Complex workflow managers
  
  

• Self-improving knowledge workers

Building Autonomous Economic Agents: Financially Independent AI

These agents operate independently in financial environments. They control on-chain wallets, interact with smart contracts, and allocate capital based on encoded logic or learned strategies.

Key Components:

• LLM Core + Economic Planner: Interprets market context or goals and determines how to act. Because there’s no off-the-shelf “economic logic” library, most teams write custom strategy code or fine-tune specific reward models.
  
  

• Crypto Wallet Access: Tied to on-chain identity; allows agents to send/receive funds and sign transactions
  
  

• Smart Contract Interaction: Can call, trigger, or query contracts on various chains like Ethereum, Solana, or BNB Chain
  
  

• Resource Allocator: Manages spending (e.g., gas costs, portfolio diversification, rebalancing)
  
  

• Economic Objective Functions: Encoded strategies or utility-maximizing behaviors (e.g., profit, TVL growth, cost minimization)
  
  

• Security & Failsafes: Rate limits, manual override switches, and spending constraints to prevent runaway financial behavior

Tech Stack:

• Wallet SDKs: Safe, ethers.js or web3.py for custody and signing
  
  

• DeFi Integrations: Uniswap SDK, Aave APIs, Gnosis Paymaster modules
  
  

• LLM + Planner: LangChain + economic logic modules or agentic wrappers
  
  

• Monitoring: Alerting tools or AI guards (e.g., GuardrailsAI, Helm, PromptLayer, etc)

Use Cases:

• Autonomous trading agents with active portfolio management
  
  

• Yield optimization agents across multiple DeFi protocols
  
  

• Treasury managers for DAOs or LLM-as-a-service systems
  
  

• Revenue-generating bots that launch and manage microservices autonomously

Building Decentralized Autonomous AI (DeAI Systems)

These are the most advanced AI agents—designed to operate autonomously and persistently across decentralized infrastructure, without any single party controlling their behavior, data, or compute. DeAI systems combine AI models (often large language models), on-chain logic, distributed compute, and community governance to create agents that act independently and evolve based on collective decision-making.

Key Components:

• LLM or AI Model Core: The reasoning engine behind the agent. While most model inference today happens off-chain, DeAI systems aim to decentralize this layer using:
  
  

  • TEE-based inference: Models run in trusted enclaves (e.g., Intel SGX) that publish verifiable attestations to the blockchain
    
    

  • Decentralized compute networks: Jobs are distributed across networks like Bittensor, Gensyn, or Akash
    
    

  • zkML (zero-knowledge machine learning): A rapidly emerging method to prove on-chain that a specific model produced a specific output without revealing proprietary data
    
    

  • IPFS or Arweave for model storage: Ensures transparency and auditability of model versions
    
    
    

• On-Chain Execution Logic: All rules governing the agent’s actions, permissions, and incentive flows are written in smart contracts. These contracts can autonomously manage treasury funds, trigger workflows, and interact with other agents or dApps.
  
  

• Distributed Hosting: Agents operate across decentralized storage, compute, and blockchain networks (e.g., Sahara Blockchain, Filecoin, Arweave, Akash), reducing reliance on any single point of failure.
  
  

• Governance Layer: Upgrades, behavior changes, or model swaps are determined by token-governed DAOs, staking communities, or hybrid governance models—ensuring that no one entity can alter the agent’s logic unilaterally.
  
  

• Reputation & Incentives: Contributors (e.g., data validators, inference nodes) earn tokens based on performance and participation, with slashing or removal mechanisms for bad behavior.
  
  

• Autonomy by Design: These agents are built to operate, evolve, and make decisions without direct human oversight. Through programmable governance and resource access, they can persist and adapt over time—even outliving their creators.

Tech Stack:

• Model Layer: Open-weight or DAO-governed LLMs + zkML or TEE-secured inference
  
  

• Blockchain: Sahara AI, Ethereum / L2s, EVM-based chains, etc
  
  

• Storage: IPFS, Arweave, Lighthouse
  
  

• Governance: DAO frameworks (Snapshot, Tally, Zodiac)
  
  

• Security / Privacy: TEEs, zero-knowledge proofs, MPC-based access control
  
  

• Execution Environment: On-chain agent contracts + off-chain compute networks with verifiable output

Use Cases:

• Decentralized prediction markets and research agents
  
  

• Community-owned AI models with provable, verifiable outputs
  
  

• On-chain agents managing collaborative compute and capital allocation
  
  

• Self-governing autonomous services that operate independently of corporations or governments

Unlike most AI systems today, DeAI agents don’t rely on a single company to host the model, define the behavior, or control the funding. Instead, they’re governed by smart contracts and communities, with model outputs increasingly verified through secure, decentralized methods. While decentralized inference is still emerging, the infrastructure is rapidly evolving. The result: agents that no one can shut down, modify, or censor, and that can coordinate, evolve, and act on their own.

Building Your Own AI Agents

The further you move down the agentic spectrum, the more complex the build becomes. While simple agents can now be created with no-code tools, building highly autonomous, multi-step, or economically independent agents still requires specialized knowledge, infrastructure, and careful orchestration. These agents are often bespoke, with custom logic, toolchains, memory systems, and execution environments.

That’s where Sahara AI comes in.

Whether you're just starting your journey or building advanced systems, we offer the tools and support to match your needs:

• For enterprises: We provide hands-on support and infrastructure for building custom, autonomous agents tailored to your organization’s workflows and data environment.
  
  

• For AI developers and AI-curious users: Our low-code and no-code Agent Builder makes it easy to create and deploy simpler agents—no ML team required. And our AI Marketplace gives you access to high-quality, verified datasets to power your agent’s capabilities, whether you’re training, fine-tuning, or building from scratch.

Agent creation isn’t one-size-fits-all. The more autonomy and intelligence you want, the more infrastructure you need, but thanks to modern tools and platforms, the barrier to entry has never been lower.

Contact us to learn more about our custom agents for your enterprise, or check out our AI Developer Platform.