AISD is an AI-native software development company that builds production AI for mid-market and enterprise teams. Three core services: AI Modernization (embedding AI into existing products — copilots, intelligent search, predictive analytics), AI Agents (autonomous workflows for support, document processing, sales outreach), and AI Workflow Automation (n8n, Zapier, Make, Clay).

How is AISD different from a typical software development agency?

Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.

How long does it take to build an AI MVP?

Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.

What does an AI MVP cost?

AISD AI MVPs typically range $45,000–$120,000 depending on scope. Drivers: number of model integrations, complexity of retrieval/data layer, custom UI surface area, and compliance requirements. We publish indicative bands on the pricing page so buyers can budget before the first call.

How do you ensure AI features are reliable in production?

Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.

How long does it take to build a production AI agent?

Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.

What does it cost to build an AI agent?

A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.

How does pricing work — fixed-price, T&M, or retainer?

All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.

Learn · AI Engineering

What is an AI agent?

An AI agent is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop. The model decides which tool to call next based on what it has seen so far.

Updated · 2026-05-04 · 6 min read

An agent vs. a chatbot

Chatbots respond and wait. Agents take action. A chatbot answering "where is my order?" reads from a knowledge base and replies with a generic delivery-time line. An agent handling the same query queries the orders API, checks the shipping system, identifies a delay, drafts a refund offer, posts it to the ticket queue, and emails the customer.

Same input — fundamentally different system. The agent's output is not just text; it's text plus a sequence of side-effecting actions.

The minimal pattern

Strip every popular framework — LangGraph, AutoGPT, CrewAI, Pydantic AI — back to the essentials, and you find three components running in a loop:

01A model. Usually a frontier-tier LLM (Claude, GPT, Gemini) for tool-using reasoning.
02A set of tools. Typed function definitions the model can invoke. Each tool has a name, description, JSON-schema input, and a runtime that returns a result.
03A control loop. Call the model with the conversation history; if the model requests a tool, run it and append the result; loop until the model produces a final answer or hits a termination condition.

Production agents add evaluation, guardrails, observability, and human-in-the-loop checkpoints around that core loop.

Real examples

Customer-support deflection agent

Reads the ticket, queries orders/shipping/billing APIs, drafts a reply, and either posts directly (low stakes) or queues for human review (refunds, escalations). Typical AISD outcome: 25–40% auto-resolution.

Document processing pipeline

Inbound contracts/claims/invoices → OCR → schema-validated extraction → routing. Exceptions go to a human queue. 30–50% reduction in human review time is typical.

Sales-research agent

For each lead: gather a recent news angle, identify the buyer persona, draft a personalized opener, write back to the CRM with a confidence score. 2–4× research throughput per SDR.

When to use an agent (and when not to)

Use an agent when the task involves multi-step reasoning + side-effecting actions — looking up data, deciding what to do with it, then doing it. Use a simpler pattern (a single LLM call, RAG over a knowledge base, a deterministic workflow) when the task is one-shot.

Agents add cost (more tokens per task, more failure modes) and complexity (eval harness, observability, prompt-injection defense). They earn that cost when the workflow has high volume and structured outputs — and don't when the workflow is rare or the output is hard to validate.

Where to go next

Frequently asked

Common questions.

What is an AI agent?
An AI agent is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop. Unlike a chatbot — which responds and waits — an agent acts, observes the result, and decides what to do next, often across dozens of steps.
What's the difference between an AI agent and a chatbot?
A chatbot turns user input into a response and stops. An agent turns user input into a plan, executes that plan by calling tools, observes the results, and revises until the goal is met or it asks for help. A chatbot answering 'what's my order status' reads from a knowledge base. An agent handling the same query queries the orders API, checks the shipping system, identifies a delay, drafts a refund request, posts it to the ticket queue, and emails the customer.
What's the difference between agentic AI and generative AI?
Generative AI is a capability: producing text, images, code, audio. Agentic AI is an architectural pattern that uses generative AI to drive autonomous, multi-step action with tools. All agentic AI uses generative AI under the hood; not all generative AI is agentic. A summarization endpoint is generative but not agentic. A customer-support agent that reads tickets, looks up orders, and posts replies is both. The agentic pattern is what unlocks measurable business outcomes.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
Where do AI agents fail in production?
Four predictable failure modes. Tool errors: an API the agent calls is down or returns unexpected data and the agent doesn't recover gracefully. Prompt injection: user-controlled text reaches the agent and overrides its instructions. Cost spirals: an agent that loops without termination conditions burns inference budget. Distribution shift: input patterns change after launch and the agent's prompts no longer match reality. Mitigations: strict tool-call schemas, prompt-injection test suites in CI, cost caps, and weekly eval re-runs.
How do you evaluate AI agent performance?
Three layers of measurement. Offline: a golden test set of 50–500 representative inputs scored automatically (model-graded) and by humans on a sample. Run on every PR. Online: per-call metrics — latency, cost, tool-call success rate, schema-validation pass rate, downstream business outcome. Human-in-loop: weekly review of escalated and low-confidence cases, fed back into the test set.
Should I use n8n, LangGraph, or build from scratch?
It depends on workflow shape and team. n8n wins when the agent is mostly orchestrating SaaS tools and the control flow is straightforward — deploys faster, easier for non-engineers to maintain. LangGraph wins when the agent has complex branching, multi-agent coordination, or needs tight Python integration with custom code. From scratch wins for simple, high-volume agents where every layer of abstraction is overhead.
What's the simplest way to start building an AI agent?
Pick one workflow with high volume, structured input/output, and a clear success metric. Map the tools the agent will need (each is an integration). Build the eval harness on day 1 — 50–500 representative inputs scored automatically. Pick orchestration based on workload, not novelty (single-loop ReAct or plan-and-execute beats multi-agent for most cases). Ship a prototype in 2 weeks, productionize in 6–10. Read /learn/what-is-agentic-ai for the full primer.

Ready to build?

From article to production agent in 6 weeks.

A 30-minute discovery call gets you to a fixed-price proposal — or an honest 'AISD isn't the right fit' if it isn't.