AI Agent Development: How We Actually Build

Scoping: Tool Inventory and Guardrail Definition

Every project starts with a precise inventory: which systems does the agent need to read from, which does it need to write to, and does each one expose a usable API or does it need a workaround. We map this against your actual workflow, not a generic checklist, because the same category of agent, a support triage agent, say, needs a completely different integration plan depending on whether you run Zendesk or a homegrown ticketing system.

In parallel, we define the guardrails: a specific allowlist of actions the agent is permitted to take, not a general instruction to use good judgment. For any action with real consequences, sending a customer-facing message, processing a payment, deleting a record, we define an approval checkpoint before the agent can execute it unsupervised. This isn't a formality; it's the difference between an agent you can trust with production data and one that's a liability waiting to happen.

Architecture Decisions

Single agent vs. multi-agent: we default to narrowly scoped single agents that do one job well, since that produces more reliable results than one generalist agent trying to do everything. We move to a multi-agent architecture, specialized agents coordinated by an orchestrator, only when a workflow genuinely spans multiple distinct domains: one agent researches, one drafts, one reviews, one publishes, each with its own clear responsibility.

LLM selection: we choose a model per project based on the reasoning complexity the task actually needs, the latency your workflow can tolerate, and cost at the volume you expect to run, not one default model applied to everything regardless of fit. A high-volume, low-complexity classification task and a low-volume, high-stakes research task have different requirements, and we scope accordingly.

Memory and state handling: we define explicitly what the agent needs to remember across a single task versus across sessions, and where that state actually lives. An agent that forgets context it needs produces inconsistent results; an agent that retains more than it needs creates a data-handling liability. We design the minimum state the task genuinely requires, not the maximum a platform makes convenient.

Build: Integration, Instruction Design, and Testing

Tool integration comes first, connecting the agent to the systems it needs to read and act on, with authentication and error handling built for production from the start, not patched in once something breaks. Instruction design, what a lot of people mean when they say "prompting," is where we define how the agent reasons through the task: what context it's given, how it should weigh conflicting signals, and what it should do when it's uncertain rather than guess.

The testing harness is built alongside the agent, not bolted on at the end. We test each branch of the agent's logic in isolation against real data from your business, then test the full pipeline end to end. For higher-stakes decisions, we run a shadow period where the agent makes its call but a person confirms before it executes, so we can compare the agent's judgment against a human's on real cases before removing that checkpoint, rather than trusting it on day one.

Deployment: Monitoring, Logging, and Rollback

Every agent ships with structured logging of what it read, what it decided, and what it did, so a result that looks wrong can be traced back to the exact input that produced it rather than treated as an unexplainable black box. We monitor closely for the first weeks after launch specifically, since real production data surfaces edge cases that even thorough testing data doesn't.

A rollback path is part of the deployment, not an afterthought: if a change introduces a regression in the agent's behavior, we can revert to the previous working version rather than debugging live against production traffic. 90 days of post-launch monitoring is included on every project; after that, ongoing maintenance moves to a retainer if you want us continuing to tune and extend the agent.

What You Actually Get at Handoff

Every project ends with documentation your team can actually read: what the agent does, what tools and permissions it has, what its guardrails are, and how to read its logs when something needs investigating. On code-first platforms, that includes the code itself, commented and structured for a developer picking it up cold. On no-code platforms, it includes a walkthrough of the workflow so a non-technical team member understands what each step does. The goal is that your team understands the system we built, not just trusts that it works, because trust without understanding breaks down the first time something behaves unexpectedly and nobody on your side knows where to look.

How We Actually Scope Cost

Three things drive the cost of an agent build more than anything else: how many distinct decision points the agent's reasoning needs to cover, how many external systems it needs to integrate with, and how much testing each of those decision points needs before we trust it against real data. A single-decision agent reading one input source and taking one action costs meaningfully less than an agent coordinating decisions across five systems with several possible outcomes at each step.

We don't publish a flat rate, because a flat rate would either overcharge simple projects or undercharge complex ones, and neither serves you well. What we do instead is give you a specific number after discovery, once we actually know what the agent needs to do, broken down clearly enough that you understand what's driving the price, not just the total at the bottom.