How to Build an AI Agent: From Prototype to Production

Key Takeaways

• To build an AI agent: define one clear goal, choose a model, connect scoped tools and data, write its instructions and guardrails, then test, deploy, and schedule it.
• An agent is only as powerful as its prompt, and only as productive as the resources you give it to act on. Both halves are the build.
• The reasoning loop is the easy part. Securing what the agent can touch, and giving it the right context, is the real work.
• You can code an agent from scratch or use a framework, but a platform path inherits identity, permissions, audit, and hosting instead of making you rebuild them.
• In Major, each step maps to a concrete piece: secure connectors, an AI-drafted system prompt, reusable Skills, and scheduled runs.

What you're actually building (and when an agent is the right tool)

To build an AI agent, you define a goal, pick a model, give it tools and data to act on, set its instructions and guardrails, then test it and ship it to production. Everything past the prototype is about controlling what it can do, not about the reasoning loop itself.

Two things decide whether an agent is any good. The prompt that tells it how to think, and the resources that let it act. A sharp prompt with no tools is a chatbot. A pile of tools behind a vague prompt is a liability. Getting both right is the whole job, and it is the thread running through every step below.

Before you build one, check that you need one. An agent earns its complexity when the task involves judgment across steps that you cannot script in advance. If the work is a fixed sequence with no real decisions, a plain workflow or a script is cheaper, more predictable, and easier to audit. OpenAI makes this point in its practical guide to building agents, and Anthropic makes it more bluntly in Building Effective Agents: start simple, and add agentic complexity only when it clearly improves the outcome. Reach for an agent when the decisions are genuine. Otherwise, write the workflow.

The six steps below are universal. What changes is how much of the production work you do by hand. At each step I show the principle, then how it comes together in Major.

How to build an AI agent, step by step

Step 1: Define one clear goal and success criteria

Write down what the agent is for in one sentence, and how you will know it worked. "Resolve refund disputes under 50 dollars by checking policy and customer history" is a goal. "Handle support" is not. A narrow goal makes every later decision easier, because it tells you which tools the agent needs and which it must never have.

In Major, that one sentence is the seed for everything else. It shapes the system prompt and decides the connectors and applications you allow. A narrow goal is also a security decision: it draws the boundary of what the agent can ever reach.

Step 2: Choose your model

Pick a model that fits the task's reasoning load and latency budget. Hard, multi-step reasoning wants a stronger model. High-volume, simple steps can use a smaller, faster one. Many real agents use more than one: a capable model for the planning and a cheaper model for routine sub-steps. Treat the model as swappable, because it will change.

In Major, the model is configuration, set alongside the agent's environment variables rather than wired into code. You can change it without rebuilding the agent, which matters because the best model for a task this quarter will not be the best one next quarter.

Step 3: Connect secure tools and data sources

This is where an agent becomes more than a chatbot, and it is the resources half of the build. An agent with no connectors cannot do anything. Connect the data it needs to read and the actions it needs to take, and scope each one to the minimum. An agent that only has to read should get read-only access. If it writes a record, that write should go through a defined, typed action rather than open API access.

This is also the step that quietly decides whether you are safe. The usual way agents leak data is a credential pasted into a prompt or a token committed to git. In Major, connectors are configured once, at the org level, by whoever owns the credentials, usually IT. Agents inherit them, so no key ever lives in a prompt or a repo. You grant an agent an allowlist of connectors, and nothing outside it is reachable. When a shared agent calls one, it runs under run-as-invoker identity, so Salesforce AI agents see the actual user who started the run and enforce that person's access. Rotate or revoke a credential once and every agent inherits the change. This is the same wiring teams use to build an AI agent for HubSpot or agents that use Notion as the data layer.

Step 4: Write the system prompt, and package context as Skills

The system prompt is the prompt half of the build, and the agent is only as powerful as it. It sets the agent's role, its constraints, and what it must refuse to do. Guardrails are the limits that hold even when the model gets creative: which tools it can call, what input is valid, what requires escalation.

Writing a strong system prompt from a blank page is hard, so in Major you do not start from one. You describe what the agent is for, and Major uses AI to draft the system prompt: the role, the allowed connectors and applications, the guardrails. You then edit it. Because the prompt is where most of the agent's behavior lives, starting from a structured draft and refining is worth the iteration.

Long instructions and repeated context do not belong crammed into one prompt. In Major you factor specialized instructions into Skills, which are versioned SKILL.md files an agent loads when it needs them. A skill might hold your refund policy, your SQL conventions, or the steps for a quarterly business review. Skills preserve context across runs and are reusable across agents, so you write the hard part once and any agent that needs it can pull it in. The result is a lean prompt plus a library of context the agent reaches for on demand, instead of one bloated instruction blob.

Step 5: Add human-in-the-loop and approvals

For any action that is expensive, irreversible, or sensitive, put a human in the path. A common pattern is an approval gate: the agent proposes the action, a person confirms it, and only then does it execute. This is not a failure of autonomy. It is how you ship an agent into a real environment without betting the business on its first run.

In Major, that gate is built in. Restricted actions can require a Slack approval before they run, and any MCP server you attach comes with a per-tool allowlist and its own approval requirements. The agent reasons and proposes, a person approves in-thread, and the action runs only after.

Step 6: Test, then deploy and schedule

Test against real cases, including the ones designed to make it misbehave. Then decide how it runs. This is where most tutorials stop, and where an agent either becomes useful or stays a toy.

In Major, deployment means giving the agent triggers. A scheduled run hands a specific agent a specific recurring task on a timer: pull last week's pipeline every Monday morning, drawing on the specific Skills that task needs and writing through the connectors it is allowed. Webhook and external-channel triggers let it run on an event or a configured Slack mention, replying in the thread. Ad-hoc runs cover one-offs. A scheduled agent with the right Skills and connectors stops being something you poke at and becomes something that does its job without you in the loop.

Code-first vs. no-code/platform: choosing your path

The steps above are universal. What differs is how much of the production foundation you build yourself.

• From-scratch code Control: Highest · Speed to prototype: Slowest · Production work required: Most; you build identity, audit, hosting yourself · Best for: Teams with specific needs and engineering capacity
• OpenAI Agents SDK Control: High · Speed to prototype: Fast · Production work required: High; the SDK is the loop, not the platform · Best for: Code-first teams standardizing on one stack
• LangChain Control: High · Speed to prototype: Fast · Production work required: High; orchestration helps, infrastructure is still yours · Best for: Composable, multi-step pipelines in code
• General No-code / platform Control: Medium · Speed to prototype: Fast · Production work required: Least; foundation handled centrally · Best for: Shipping safely without rebuilding the basics
• Major Control: High · Speed to prototype: Fastest · Production work required: Least; foundation handled centrally

The code-first routes are real and good. A from-scratch build gives you maximum control and the most production work, because you own identity, permissions, audit, and hosting end to end. The OpenAI Agents SDK and LangChain give you the agent loop and useful patterns, which is a head start on the reasoning and very little of the infrastructure. As IBM notes in its how-to, frameworks get you a prototype quickly. They do not get you a production system. Major is not a competitor to those frameworks. It is the platform the agent runs on, and Claude Code, Cursor, and Codex can sit on top of it.

What the tutorials skip: making it production-ready

Almost every guide ends at a working loop. The working loop takes an afternoon. Turning it into something you can run against real systems takes the rest of the project. Here is the checklist that separates the two, and in Major each item is inherited from the platform rather than rebuilt per agent.

• Identity: the agent should act as a real principal. When a shared agent calls a connector, it uses the identity of the user who started the run, so upstream systems see the right person and enforce the right access.
• Scoped permissions: access is enforced at the query layer, not as a setting in a UI. The agent gets the minimum it needs and nothing more.
• Audit logging: every action the agent takes leaves a record you can export to your SIEM, the same record any other software action would leave.
• Observability of decisions: you need to observe what the agent decided and did, rather than only the model's tokens. At the moment of action, what context did it have, what tools were available, what did it choose?
• Cost controls: token spend, rate limits, and stopping conditions, so a runaway loop is capped rather than billed.

If you cannot answer those five for your agent, you have a prototype, not a production system. That is fine, as long as you are honest about which one you have.

Common mistakes

The failures repeat. Over-broad connector scopes, where an agent that needs to read is also able to write, or delete. Cramming every instruction into one giant system prompt instead of factoring the reusable parts into Skills. No evaluations, so you find out it regressed from a customer rather than a test. Granting too much autonomy too soon, before you have watched it behave on real cases. And skipping logging, which turns the first incident into a guessing game because nothing was recorded. Each one is avoidable, and each one is cheaper to fix before launch than after.

Build an AI Agent in Major

Here is one agent built end to end, and a familiar one: an agent that drafts replies to incoming customer support emails. When a new email arrives, the agent reads the message, pulls the customer's recent orders and account history, and checks your help-center answers. It reasons about what the customer actually needs, then drafts a reply. For a routine question it sends the reply through a deterministic app; for anything sensitive, like issuing a refund, it pauses for a one-click Slack approval before it acts.

Trace it back to the two halves. Its power comes from the prompt: an AI-drafted system prompt that sets the agent's tone and what it must never promise, plus a Skill holding your support and refund policies. Its productivity comes from the resources: the email and customer-record connectors configured once at the org level, run-as-invoker identity so it only sees what the handling agent is allowed to see, a deterministic Send Reply app that logs every message it sends, and a Slack approval gate on the actions that matter. Every reply and refund is audit-logged and exportable to your SIEM.

Major is the OS for Enterprise Agentic AI, which here means one thing: the agent does the reasoning, and a deterministic, audited app does the acting, on a foundation of identity, permissions, and audit that was in place before the first prompt. An agent is only as powerful as its prompt and only as productive as its resources. The reasoning is the easy 20 percent. The prompt, the Skills, the secure connectors, and the schedule are the other 80 percent, and they decide whether your agent ever leaves your laptop.