Question 1

What is the difference between an AI agent and a chatbot?

Accepted Answer

A chatbot returns text. An agent takes actions. A production agent decides which tool to call, calls it, reads the result, recovers when something fails, and repeats until the task is done, with a human approving the steps that carry real consequences. The systems built here read records, run calculations, write to CRMs, and generate documents, not just answer questions.

Question 2

Claude Agent SDK or LangGraph, which do you use?

Accepted Answer

Both are good, and the choice follows the problem rather than a preference. Claude Agent SDK is a strong default for tool-calling agents with clean guardrails and budgets. LangGraph earns its place when you need explicit, inspectable state machines with complex branching. Most systems here run on the Claude Agent SDK, and the decision is made in the assessment against your requirements.

Question 3

How do you keep agents reliable in production?

Accepted Answer

Reliability is designed in, not bolted on. Structured outputs so every response is schema-valid, retries and fallbacks so a single failed call does not sink the task, human-in-the-loop gates on high-stakes actions, an evaluation harness that catches regressions before release, and observability so you can see exactly what the agent did and why. One shipped system ran six months in production without a single malformed output.

Question 4

How long does it take to build an AI agent?

Accepted Answer

The documented range across shipped systems is 4 to 8 weeks from kickoff to production, and a working proof of concept on your own data usually lands within the first week. A cybersecurity query agent took 4 weeks, a HIPAA-compliant appeals system took 5, and a company-wide platform serving more than 200 locations took 8.

Question 5

Who owns the code and the prompts?

Accepted Answer

You do, completely. Source code, prompts, evaluation suites, documentation, and infrastructure configuration are delivered into your repositories as they are written. There are no black boxes and no license-back clauses. If we parted ways the day after launch, your team could run and extend everything on its own.

	Typical agency or DIY	SASID
What ships	A chatbot wrapper around one prompt	Autonomous agents that call tools and act
Reliability	Breaks on the first unexpected input	Retries, fallbacks, and human-in-the-loop gates
Proof it works	A scripted demo	An eval harness and observability from day one
Who builds it	Junior team, months of billing	One senior engineer, live in 4 to 8 weeks
Reaching production	Roughly half of AI pilots never do	A 7 for 7 record of production deployments
Ownership	Vendor lock-in, black box	You own all code, prompts, and evals

AI Agent Development That Reaches Production.

What a production agent build includes

Agents that act, not chat

Safe by construction

Production infrastructure

Proof from shipped agent systems

AI agent development vs the alternatives

Three ways to start

Proof-of-concept sprint

Production build

Fractional advisory

What it costs

AI agent development questions, answered directly

See your agent working on your own data before you commit.