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AI Agents: What They Are, What They're Not, and Why You Should Care

February 6, 202610 min readCoraLabs Team

First, Let's Kill Some Hype

Every chatbot vendor is now calling their product an "AI agent." Let's be clear about what we mean: an AI agent is a system that can think through a problem, make a plan, use tools, and execute multi-step tasks without someone holding its hand.

That's different from a chatbot that pattern-matches your question to a FAQ entry. Way different.

A good AI agent can:

  • Understand complex instructions in plain English
  • Break a big goal into smaller steps
  • Use tools (APIs, databases, code execution) to get things done
  • Learn from what worked and what didn't
  • Know when to escalate to a human

    • How They Actually Work

    Under the hood, modern agents run on a loop:

    The Core Loop

  • Observe - get a task or notice a trigger
  • Think - figure out the best approach (chain-of-thought reasoning)
  • Act - do something (call an API, query a database, run code)
  • Check - look at the result and decide what's next

    • The Key Pieces

  • LLM backbone - the reasoning engine (GPT-4, Claude, or open-source alternatives)
  • Tool integration - connections to your APIs, databases, file systems
  • Memory - both short-term (this conversation) and long-term (stored knowledge)
  • Planning - how it breaks tasks down and sequences them
  • Guardrails - safety checks, output validation, and "ask a human" triggers

    • What Businesses Are Actually Using Them For

    Customer Support

    Not FAQ bots. Agents that understand the problem, look up account details, check order status, apply fixes, and write a coherent response. The kind of support that used to require a senior rep.

    Data Work

    "Analyze last quarter's sales data and flag anything weird." The agent grabs the data, cleans it, runs the analysis, makes charts, and writes up findings. What used to be a half-day task becomes a 5-minute request.

    Engineering Assistance

    Copilots that understand your codebase, suggest fixes, run tests, generate docs. Not replacing engineers, but giving them a really fast assistant who's read all the documentation.

    Sales Outreach

    Agents that research prospects, draft personalized emails, qualify leads based on your criteria, and book meetings. They work at 2 AM and they don't need motivational posters.

    Build or Buy?

    QuestionBuild CustomUse a Platform
    How flexible?Total controlLimited to what they offer
    Upfront cost?HigherLower
    Fits your domain?PerfectlyDepends
    Time to deploy?Weeks/monthsDays
    Who maintains it?YouThey do

    For most companies, the answer is: start with a platform to prove the concept, then build custom when you know exactly what you need.

    The Stack

    If you do build, you'll probably use some combination of:

  • LangChain / LangGraph for orchestration
  • OpenAI / Anthropic APIs for the reasoning
  • Vector databases (Pinecone, Weaviate) for long-term memory
  • FastAPI / Python for the backend
  • Redis for caching

    • Getting Started Without Overcomplicating It

  • Find the right workflow - look for tasks that are repetitive but require some judgment
  • Start with one thing - resist the urge to automate everything at once
  • Keep humans in the loop - especially at first. Let the agent earn your trust
  • Measure what matters - time saved, error rates, cost per task

    • We build custom AI agents at CoraLabs. If you've got a workflow that's eating up too many hours, let's talk about it.

    Ready to get started?

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