Question 1

What's the difference between an AI agent and a chatbot?

Accepted Answer

A chatbot answers questions in a conversation; an agent pursues a goal — it plans steps, calls tools and APIs, checks intermediate results, and continues until the task is finished. That makes agents suitable for work like triaging tickets end to end, compiling research reports, or reconciling data across systems, where a chatbot would stop at giving advice.

Question 2

How reliable are AI agents in production?

Accepted Answer

Reliability depends less on the model and more on engineering: constrained tools, structured outputs, validation of every step, and retries with fallbacks. With those in place, well-scoped agents routinely complete the large majority of runs without intervention — and the remainder escalate to a human instead of failing silently. We measure completion rates on your real cases before launch.

Question 3

How long does it take to build a production agent?

Accepted Answer

A focused single-task agent typically takes 4–8 weeks from discovery to production, including evaluation and a pilot phase. Multi-agent systems spanning several departments or tools run 2–4 months. The biggest schedule variable is usually API access to your internal systems, not the AI itself.

Question 4

What does it cost to run an agent?

Accepted Answer

Agent runs consume far more tokens than simple chat because of planning and tool loops — a complex task can use 50,000–500,000 tokens. Depending on the model, that ranges from a fraction of a cent to a few dollars per task. We set per-run token budgets, cache repeated context, and route easy steps to cheaper models to keep unit costs predictable.

Question 5

Which model do you use — GPT, Claude, or open source?

Accepted Answer

We benchmark candidates on your actual tasks rather than picking by reputation. Frontier models like Claude and GPT-4-class models handle complex reasoning best, while smaller open-source models often win on cost for high-volume, narrow steps. Many of our agents use a mix: a strong model for planning, cheaper models for routine sub-tasks.

Question 6

Can agents take actions in our systems safely?

Accepted Answer

Yes, with layered controls: agents only get the specific API permissions they need, destructive actions require human approval, and every action is logged and reversible where possible. We typically start agents in read-only or draft mode and expand write access only after the pilot proves accuracy.

Question 7

What happens when an agent gets something wrong?

Accepted Answer

Errors are designed for, not just hoped against. Failed validations trigger retries with adjusted context; repeated failures escalate to a human with the full trace of what the agent did and why. Every failure case feeds the evaluation set, so the same mistake is caught in testing next time.

Question 8

When is an agent the wrong solution?

Accepted Answer

If the workflow is fully deterministic — same inputs, same steps, every time — classic automation or RPA is cheaper and more reliable. Agents earn their cost when tasks need judgment, unstructured inputs, or dynamic multi-step planning. In discovery we'll tell you honestly which category your workflow falls into.

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