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

How agentic AI works

Agentic AI is software that uses a language model to plan and take multi-step actions toward a goal. It observes, reasons, acts, and loops until the job is done. Five components make this work. Understanding them is the difference between a demo agent and a production one.

Updated · 2026-05-02 · 7 min read

Architecture

Five components of an agentic system

Component 01

The language model (reasoning engine)

A frontier LLM (GPT-5, Claude 4, Gemini 2.5) or open-weight model (Llama, Mistral) that processes observations and decides what to do next. The model doesn't just answer questions. It reasons about goals, selects tools, and evaluates results.

Component 02

The tool set (actions)

APIs, databases, file systems, browsers, and any other system the agent can interact with. Tools turn the model's decisions into real-world effects. Without tools, you have a chatbot, not an agent.

Component 03

The control loop (orchestration)

The logic that iterates: observe → think → act → observe. The loop continues until the goal is met, a human is needed, or a safety bound is hit. This is the part that makes it agentic.

Component 04

Memory (state management)

Short-term memory (context window) plus long-term memory (vector stores, databases). Memory lets agents maintain context across steps and sessions. Without it, every loop iteration starts from scratch.

Component 05

Safety boundaries (guardrails)

Max iterations, budget caps, scope constraints, human-in-the-loop checkpoints, and prompt injection defenses. Production agents without guardrails are a liability. Build them in, not on.

In production

Where agentic AI delivers ROI today

Customer support

Agent reads ticket, looks up order status, checks refund policy, issues refund, sends email. End-to-end resolution without human touch for 40-60% of tickets.

Document processing

Agent ingests contracts/invoices, extracts structured fields, validates against business rules, posts to ERP. Handles thousands of documents per day with human review only on exceptions.

Sales research

Agent monitors news signals, gathers prospect data from multiple sources, drafts personalized outreach, logs activity to CRM. Frees reps to sell instead of research.

Healthcare admin

Agent processes clinical documentation, extracts diagnoses and procedures, maps to billing codes, flags discrepancies. Reduces coding time by 47% (see our case study).

The key insight

Agentic AI is not smarter AI. It is AI with agency.

The language model inside an agent is the same model you use in ChatGPT. What makes the system agentic is the architecture around it: the tools it can call, the loop that keeps it going, the memory that maintains context, and the guardrails that keep it safe.

This means you don't need to wait for a "better model" to build production agents. You need better orchestration, better eval, and better safety design. Those are engineering problems, not research problems.