AI Agents in Business: What They Are, What They Aren’t, and When They’re Worth It

The term "agent" has been overused in 2024 just as "AI-powered" was in 2023. Any form that makes a single call to the OpenAI API is now marketed as an agent. Let’s separate the marketing fluff from genuine engineering.

What a Real AI Agent Actually Is

A true AI agent is a system that:

1. Has a defined goal beyond just responding to a message.
2. Makes decisions about which tools or actions to use at each step.
3. Maintains context across interactions (memory, state).
4. Recovers from errors by retrying with alternative strategies.

If your "agent" makes one API call to an LLM and returns the response, it’s not an agent—it’s a wrapper. There’s nothing wrong with wrappers, but let’s call them what they are.

Case Study: Tontin — The Reality Behind a Working AI Agent

Tontin is our AI-powered professional coaching platform, featuring both a chatbot and a Chrome extension. From the outside, it looks like "just another AI assistant." From the inside, it’s built differently:

• Multiple specialized agents with distinct prompts and memories: the resume advisor, the web consultant, and the coding buddy. Each has its own knowledge base.

• Persistent memory across sessions. If you return the following week, the system remembers where you left off.

• Context compression: A custom layer reduces historical context by 50% before each call—without losing key details. This cuts costs in half.

• Fallback chain between providers: If Groq is down, it switches to Gemini. If Gemini is saturated, it moves to Anthropic. The user never notices.

• Semantic search with embeddings over past conversations using pgvector within Postgres.

• Feedback loop: Users can mark responses as helpful or not, and this data feeds back into the system.

After over 500 completed conversations, all of this has been battle-tested in production. That’s the real cost of a working AI agent.

When It’s Worth Building an AI Agent for Your Business

A well-built agent makes sense if:

• The process is repetitive but requires contextual reasoning (not just if-else logic).
• The cost of failure is low or bounded (it suggests, doesn’t make irreversible decisions).
• You have sufficient volume to justify the upfront development effort.

Good use cases:

• Ticket classification and routing
• Drafting proposal outlines
• Internal assistants over company documentation
• Meeting summaries with extracted action items

Not ready for prime time:

• Unsupervised financial decisions
• High-stakes customer support without human escalation paths
• Any scenario where an unfiltered AI error could cause real harm

The Hidden Costs No One Talks About

Building an AI agent isn’t just about the initial development. In production, it includes:

• LLM call costs (each interaction consumes billed tokens).
• Storage for embeddings and persistent memory.
• Quality monitoring: Regularly reviewing samples to catch regressions.
• Maintenance: Models evolve—what worked three months ago may behave differently now.

Costs vary based on approach. Using open-source models and services with free tiers (Groq, Supabase, Vercel), you can launch a functional agent with near-zero upfront cost. What does cost is engineering time and ongoing maintenance.

Final Thoughts

AI agents are one of the rare technologies that truly live up to the hype—if they’re built right. A poorly built agent is worse than nothing: it confuses users, delivers bad results, and drains your team’s patience. Before jumping in, clearly define your use case, success metrics, and maintenance plan.

If you have a process that repeats and think AI could help, reach out. In a quick call, we can assess whether your needs call for a full agent, a simple wrapper, or something in between.

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By Esteban Aleart, Founder & Lead Engineer at Pair Programming.