Tag: llm

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AI Tools versus AI Agents: What’s the Difference.

Yogi Nelson

🤖

Welcome to the BlockchainAIForum where your technology questions are answered. Artificial Intelligence is everywhere, but the terms we use to describe it can be confusing. Two terms that often get mixed up are AI tools and AI agents. Though they sound similar, they reflect fundamentally different ideas. Therefore, today we explore the following question: AI Agents vs AI Tools: What’s the Difference?, and we do so in my usual way–friendly and jargon free.

🛠️ What Is an AI Tool?

An AI tool is like any other software tool—it’s designed to help you perform a task better or faster. Think of AI tools as advanced assistants that you control directly. They don’t make big independent decisions; they simply do what you tell them.

Examples of AI tools:

  • ChatGPT in its “normal” form (you give it a prompt; it gives you an answer)
  • MidJourney or DALL-E (you enter a description; it generates an image)
  • AI summarizers or translators

Key traits of AI tools:

  • User-directed: You have to tell them what to do, step by step.
  • Single-task focus: They do one thing at a time.
  • Predictable responses (usually): You know what you’re going to get most of the time.

Blockchain analogy: Think of an AI tool like a blockchain wallet. It doesn’t move your funds on its own. You sign the transaction; the wallet just executes it for you.

🧭 What Is an AI Agent?

Now let’s talk about AI agents. These are AI systems designed to act autonomously to accomplish goals. Instead of just responding to your commands, they can figure out how to achieve a result, choosing from multiple steps or strategies.

Examples of AI agents:

  • A travel-booking agent that can compare flights, hotels, and book the best options automatically
  • Customer-support bots that handle entire conversations end-to-end
  • Research assistants that plan and execute multi-step tasks (e.g., searching sources, summarizing, writing a draft)

Key traits of AI agents:

  • Goal-directed: You tell them what you want, not how to do it.
  • Autonomous: They plan and carry out steps on their own.
  • Adaptive: They may change approach if they hit an obstacle.

Blockchain analogy: If an AI tool is a wallet, an AI agent is like a smart contract that can execute a whole set of instructions once triggered, without constant human intervention.

📊 Side-by-Side Comparison

FeatureAI ToolAI Agent
User ControlFully manual, step-by-stepHigh-level goals given
AutonomyNone or minimalSignificant, plans its own steps
ComplexitySingle-step tasksMulti-step workflows
AdaptabilityLowHigh

🤝 Why Does This Difference Matter?

This isn’t just academic hair-splitting. The distinction shapes how we use, trust, and regulate AI.

Ease of Use vs. Risk

  • Tools are easier to understand and audit because they’re direct extensions of your command.
  • Agents can save time but may act unpredictably or in unintended ways.

Integration with Blockchain

  • AI tools can be combined with blockchain for straightforward tasks, like verifying data or signing transactions.
  • AI agents could manage entire decentralized processes—think DAO treasury management, contract negotiations, or supply-chain orchestration. That introduces both opportunity and risk, requiring new kinds of governance.

💡 How to Choose Between Them

When you’re thinking about adopting AI in your workflow or project:

✅ Use an AI tool if:

  • You want tight control.
  • Your task is simple or single-step.
  • You want easy auditing.

✅ Use an AI agent if:

  • The task requires multiple steps.
  • You’re okay with some autonomy.
  • You want to delegate strategy, not just execution.

🌐 The Future: Agents Built on Tools

The lines between tools and agents are also blurring. Many AI agents are built out of multiple tools working together. For example, an AI agent that researches for you might use:

  • A search API (tool)
  • A summarizer (tool)
  • A planner (the agent itself)

The most exciting future AI systems will combine these elements seamlessly, much like smart contracts combine blockchain primitives.

🤖 Final Thoughts

As blockchain and AI continue to merge, understanding this distinction will be essential. Whether you’re building decentralized science tools, blockchain marketplaces, or AI-driven DeFi agents, you’ll need to decide:

Are you building a tool that helps people do things better?
Or an agent that can do things for them?

That decision will shape not just your technology—but your responsibilities to your users and your community.

I end with a proverb from where they say: “A single bracelet does not jingle”. Share your thoughts below or on BlockchainAIForum.com.

Until Next Time,

Yogi Nelson

AI Agents, AI Tools, Blockchains, cryptography, Uncategorized, Yogi Nelson

Building Effective AI Agents: A Complete Guide

Yogi Nelson

Welcome to the Blockchain & AI Forum, where your technology questions are answered.  Today’s question: are there practical tips for building an artificial intelligence (AI) agent?  Answer—yes.  Today I’ll explain, starting with what is an AI agent.

What is an AI Agent.  AI agents are poised to become ubiquitous.  Says who?  All the IT giants, e.g. Amazon, Microsoft, Google, etc., that’s who. Let’s start by defining AI agents.  According to IBM, AI agents:

a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools.  AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions. AI agents can be deployed in various applications to solve complex tasks, including software design, IT automation, code-generation tools and conversational assistants. AI agents use advanced large language models (LLMs) to comprehend and respond to user inputs.

Knowing When to Build an AI Agent. Open AI, the company behind ChatGPT, says building AI agents requires rethinking how existing systems make decisions and handle complexity.  Therefore, if you find yourself dealing with the situation below, you have an ideal circumstance for introducing AI agents, says Open AI:

Click to access a-practical-guide-to-building-agents.pdf

  1. Complex Decision Making.    Workflows involving nuanced judgment, exceptions, or context-sensitive decisions, e.g. refund approval in customer service workflows.
  2. Difficult to Maintain Rules.  Systems that have become unwieldly due to extensive and intricate rule sets, making updates costly or error-prone, e.g. performing vendor security reviews.
  3. Heavy Reliance on Unstructured Data.  Scenarios that involve natural language, extracting meaning from documents, or interacting with users conversationally, e.g. processing a home insurance claim.

AI Agent Design Foundations.  Fundamentally, AI agents consists of three core components: 1) model; 2) tools; and 3) instructions.  Let’s briefly define each core component.  Model refers to the large language model powering the agent’s reasoning and decision making. Tools means the external functions or APIs the agent can use to take action.  And instructions are the explicit guidelines and guardrails defining how the agent behaves. Next we examine principles for selecting a model.

Principles for Choosing a Model.  Not all AI models are identical.  Models vary by capabilities.  And, of course, not every task requires the best model.  Ideally build your AI agent with the most capable model for the task, factoring in trade-offs related to task complexity, latency, and cost.  With those considerations in mind, use the following methodology:

  1. Set up evaluations to establish a performance baseline.
  2. Focus on meeting your accuracy target with the best model available.
  3. Optimize for cost and latency by replacing larger models with smaller ones where possible.  If you want an Open AI model, visit this link:  https://platform.openai.com/docs/guides/model-selection

What are AI Tools.  AI tools extend the capabilities of AI agents by using APIs from underlying applications or systems.  Tools should have a standardized definition, enabling flexible, many-to-many relationships between tools and agents.  AI agents need three types of tools:

  1. Data.  Data enables AI agents to retrieve context and information necessary for executing workflow.
  2. Action.  Action tools enable agents to interact with systems to take actions, i.e., adding new information, updating records, or sending messages.
  3. Orchestration.  This is where it gets a bit science fiction.  Orchestration allows AI agents themselves to serve as tools for one or more AI agents!  When to use multiple agents?  When the single agent model fails to follow complicated instructions or consistently selects incorrect tools.

Best Practices for AI Agent Instructions.  Without high quality instructions, your AI agent will be suboptimal.  Below are a few suggestions for creating high quality instructions:

  1. Use existing documents. 
  2. Prompt the AI Agent to break down the tasks into smaller more manageable steps.
  3. Define clear actions.  In other words, make sure every step corresponds to a specific action.
  4. Capture edge cases.  Not everything fits in a box and sometimes information is missing.  Hence, instructions should anticipate common variations and include instructions on how to handle the non-routine with conditional steps.

Guardrails.  What are guardrails?  Essentially, guardrails are layered defense mechanism.  When properly designed, guardrails help manage risk.  Consider three factors when constructing guardrails:  1) privacy and safety; 2) add new guardrails based on real world experience; 3) optimize the balance between security and user experience.  With that in mind below are possible AI agent guardrails:

  1. Relevance Classifier.  This ensures the AI Agent stays within the intended scope by flagging off-topic queries.
  2. Safety Classifier.  These detect unsafe inputs that attempt to exploit system vulnerabilities.
  3. PII Filter.  PLL filters prevent unnecessary exposure of personally identifiable information.
  4. Moderation.  Moderation guardrails flag harmful or inappropriate inputs.
  5. Tool Safeguards.  With tools safeguard you can assess the risk of each tool available to the AI Agent.
  6. Rules-Based Protections.  The idea behind rules-based protection is to use simple deterministic measures to prevent known threats.  
  7. Output Validation.  Ensure responses align with brand values via prompt engineering and content checks.

Until next time,

Yogi Nelson and his AI Agent

Uncategorized

SHOULD YOU BE WORRIED IF YOU LAWYER USES ARTIFICIAL INTELLIGENCE (AI)

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Esquire Nave and he wants to know if he should be worried if his lawyer uses artificial?

Attorney Nava, you came to the right place.  Great question I found an interesting article on this subject, titled AI on Trial, Legal Models Hallucinate in 1 out of 6 Queries. https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-queries. The report was produced by Stanford’s Human AI Center.  Let’s examine their findings and the implications for the legal profession, beginning with basic findings of fact.

Large language models such as ChatGPT, are not ready for prime time in the courthouse, is the headline.  Stanford’s HAI Center study reports error rates were as high as 69%.  Holy inadmissible evidence Batman! U.S. Supreme Court Chief Justice felt compelled to make a point regarding this problem in his 2023 Annual Report on the Judiciary https://www.supremecourt.gov/publicinfo/year-end/2023year-endreport.pdf

Is there a solution?  People working on retrieval augmented generation (RAG) say yes, and they have it.  According to RAG advocates, RAG reduces hallucinations in domain-specific contexts. RAG proponents claim they can avoid hallucinations and can guarantee legal citations.  Essentially, RAG systems promise to deliver more accurate and trustworthy legal information by integrating a language model with a database of legal documents. Can RAG advocates deliver on these bold claims?  Not yet says, Stanford’s HAI Center.

Stanford put two RAG providers to the test, Lexus-Nexus and Thomson-Reuters.  Stanford found RAG providers are less prone to hallucinations when compared to ChatGPT, etc.  However, given that non-RPG models produced an error rate of 69% that wasn’t much of an accomplishment.  According to Stanford, RAG models hallucinated 17% (1 of 6) when prompted.  Which brings us to the next question, what types of queries were asked of the RAG models.  Answer, there were four basic types.

The Stanford HAI Center says they prompted the models with the following four types of queries:

1) general research questions (questions about doctrine, case holdings, or the bar exam);

2) jurisdiction or time-specific questions (questions about circuit splits and recent changes in the law);

3) false premise questions (questions that mimic a user having a mistaken understanding of the law); and

4) factual recall questions (questions about simple, objective facts that require no legal interpretation). 

The RAG models fell into one of two types of hallucination errors.

The first was to describe the law incorrectly or make a straight factual error.  The second type of error was mis-grounded.  When mis-grounded errors were produced, the AI model describes the law correctly, but cited source which did not support its claims.  Either way, a problem; a big problem.  Mis-grounded hallucinations might be more troublesome because they undermine the point of AI in legal research; namely, AI reduces the time required to conduct research.

Stanford identified three challenges facing RAG model developers.  First, legal retrieval is difficult because the “law” is more than verifiable facts; it also consists of rulings and interpretations.  Second, the complexity of American jurisprudence means retrieval of inappropriate authoritative documents happens often. Last, sycophancy is a concern.  Sycophancy is the tendency of RAG models to agree with the user’s incorrect assumptions. 

Bottom line–RAG models are superior to ChatGPT but require greater transparency, refinement, and not yet ready for Broadway.

We end with a proverb from Estonia:  One fool can ask more questions than 10 men can answer.

Until next time,

Yogi Nelson

Artificial Intelligence, Blockchains, France, Yogi Nelson

A GLIMPSE AT MISTRAL—THE FRENCH LEADER IN THE AI REVOLUTION

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Luis from Malibu and he wants to know is Mistral leading the French AI revolution?

Luis, you came to the right place. Your question has perfect timing. I just returned from France and am feeling the French vibe! Rather than covering the entire waterfront of AI developments in France, I’ll limit myself to a French company named Mistral.  Let’s start with a bit of background on Mistral, including its name which is French to the core–c’est la vie!

The word mistral has multiple uses in French.  Not only does Mistral mean a cold northwesterly wind, but it’s also not uncommon for companies in industries related to wind energy, sailing, or the environment to use it in their name. The French say it evokes a sense of power, speed, and a connection to the natural world. It can also mean masterly.  While traveling in France, I noticed Mistral as a last name as we say in the USA or as they say in France, the family name.  Holy multi-tasking word, Batman!  Now a word about the Mistral team and company mission.

With fewer than two dozen members, the Mistral team is small.  According to Mistral, their mission is to make frontier AI ubiquitous, and to provide tailor-made AI to all developers.  Mistral says, their mission requires fierce independence, strong commitment to open, portable, and custom solutions, and an extreme focus on shipping the most advanced technology in limited time.  How I discovered Mistral comes next, followed by what Mistral does, and what it offers.

Approximately three months ago I discovered Mistral while researching AI projects for this blog.  I attempted access but was denied.  Instead, Mistral put me on a waiting list of interested users and recently granted me access with a full disclosure that their large language model (LLM) is in beta testing status. 

Today, I put Mistral’s LLM, Le Chat, to the test against, two American companies—Open AI’s ChatGPT and Microsoft’s Co-Pilot– by asking all three identical questions.  All three-offer free access to their LLM services.  I won’t detail the contest; it’s not necessary.  Essentially, Mistral is lacking behind the competition.  Why Mistral trails, is a matter of speculation.  Perhaps its funding related?  Or maybe Mistral team size means it’s too small to compete with the titans? Could be they started later?  I don’t know, but I do know the competition is ahead.  Okay, now we go beyond their LLM to a preview of Mistrals AI products. 

On their website, Mistral has a click option labeled La Plataforme (the Platform).  This is where you’ll find the heart of their offerings and services.  Basically, La Plataforme is a subscription service.  For a fee, Mistral offers access to their AI developer tools. Mistral claims the tools permits users to develop AI agents and other related products.  The subscription grants access to the latest Mistral models and to pay based on what you use.  That’s a good feature.  Moreover, users can set monthly spending limits and if there are multiple users with an enterprise, Mistral will centralize billing.  The subscription grants access to the corresponding documentation and of course users can create API keys to access Mistral AI.

Time to say “au revoir” (goodbye) but not before sharing this French proverb:  Only imbeciles don’t change their opinions.”   Well said, my French friends!

Sincerely,

Yogi Nelson