AI & Tech
Agents, explained — without the hype, and without the jargon
A grounded introduction for operating leaders, risk officers, and the engineers being asked to build with this stuff. No marketing, no breathless predictions.
Start with the definition
An AI agent is a software system that decides what to do next, on its own
That sounds simple, and at the level of definition, it is. The complication is everything that distinction implies for how you build, govern, and operate one.
Traditional software follows a script you wrote. Given the same inputs, it produces the same outputs. An agent, by contrast, is given a goal and a set of tools, and figures out the path itself. That gives it the ability to handle inputs the script-writer never imagined — and the responsibility to fail safely when it encounters one.
The whole discipline of building production agents — what we do — is about giving them just enough autonomy to be useful, and not a single millimeter more.
Vocabulary
Five terms worth knowing before the rest of this page
Agent
A software system that, given a goal, decides on its own what actions to take, executes them using tools, observes the results, and decides what to do next — until the goal is achieved or it determines it cannot complete the work.
Tool
A capability the agent can invoke: an API call, a database query, a calculation, a search, a message to another agent. Tools are the agent's hands.
Orchestration
The layer that coordinates multiple agents, routes work between them, manages state across long-running tasks, and provides the safety rails that production work requires.
Evaluation harness
A test suite for the agent — a curated set of inputs with known correct outputs, used to score the agent's accuracy before and after every change.
Human-in-the-loop
A workflow design where the agent escalates certain decisions or actions to a human for approval before proceeding. The most important question in agent design.
How an agent actually runs
The agent loop, in six steps
Goal
A human (or another agent, or a triggering event) gives the agent a goal: "process this invoice", "investigate this alert", "respond to this customer".
Plan
The agent considers the goal and forms a plan — a sequence of actions it believes will achieve the goal. Plans can be single-step or multi-step.
Act
The agent executes a step using one of its tools — calling an API, querying data, asking a clarifying question, or escalating to a human.
Observe
The agent observes the result. Did the API return the expected data? Did the database update? Was an error returned?
Reason
The agent updates its understanding based on the observation, and decides whether to continue, replan, or escalate.
Loop
Steps 3–5 repeat until the goal is achieved, the agent decides it cannot achieve the goal, or the agent triggers an escalation.
Common misconceptions
Four things you'll hear that are wrong (or at least incomplete)
Myth
Agents are just bigger chatbots
Reality
Agents take actions in the world. Chatbots converse. The difference matters: when an agent is wrong, it can move money, change records, or trigger downstream events. When a chatbot is wrong, it generates a bad sentence.
Myth
Agents will replace your operations team
Reality
Well-designed agents augment operations teams by handling the routine 80% of work. The remaining 20% — the cases requiring judgment, exception handling, and customer empathy — is exactly what your best operators are good at.
Myth
Agents are non-deterministic and therefore not auditable
Reality
Production agents log every input, every reasoning step, every tool call, and every output. The decision is reconstructable. We routinely walk auditors and regulators through specific decisions made months earlier.
Myth
You need a custom-trained model to build a useful agent
Reality
For most enterprise workflows, the best frontier model with well-designed tools, retrieval, and orchestration outperforms a fine-tuned smaller model — and is cheaper to maintain.
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