TECHNOLOGIES

AI Agents

Software that uses an LLM to decide what to do next, calls tools to act on those decisions, and loops until a goal is reached.

Last reviewed: 2026-05-24 byKevin Riedl wiki β†—

An AI agent is the loop, not the model. The model (Claude, GPT, Gemini, an open-weights LLM) is the decision-maker. The agent is the runtime: it gives the model a goal, lets it call tools (a database, an API, a code interpreter, another agent), observes the result, and feeds the loop until done.

The distinction matters because agentic systems fail differently from straight LLM applications. A chatbot can be wrong and the user moves on. An agent that is wrong sends an email, makes a transaction, or deletes a file. Production agents need authorisation gates, observability, and circuit breakers that most prototypes skip.

Wavect’s posture: start by asking if AI is the right tool here, then if an agent is the right shape of AI for it. Most workflows that get pitched as "agentic" are better served by a deterministic pipeline with one LLM call in the middle.

// FAQ

FAQs

FAQs

Whenever a deterministic pipeline with one or two LLM calls in the middle would do the job. Agents add non-determinism, latency, and a much bigger blast radius when they misfire. If your workflow is well-understood, do not let the model improvise the steps.
Unbounded tool use. The agent loops, calls the same tool ten times, burns the budget, and never reaches the goal. Production agents need step limits, cost ceilings, and circuit breakers. Without those, a single bad prompt becomes a billing incident.
For anything destructive or irreversible: yes. Sending an email, making a payment, deleting a record, posting publicly, hitting an external API with side effects. Read-only loops can usually run unattended; write-side actions should have a gate.