01
Customer support agents
24/7 ticket triage, draft replies, and resolution. Reads the customer history, queries internal systems, posts back to your help desk. Hands off to humans on edge cases — by design, not by bug.
AI Agent Development
AI agents that ship — not chatbot demos.
We build autonomous AI agents that integrate with your tools, execute multi-step tasks, and recover gracefully when things go wrong. Production-grade, with eval harness and observability built in. From $40,000, in 6–10 weeks.
30+ agents shipped · LangGraph / n8n / Pydantic AI · Eval-harness from day 1
Six types of AI agent
Pick a category. We'll architect the build.
02
Document-processing agents
Extract structured data from contracts, invoices, claims, and reports. Validate against business rules, route exceptions. Cuts adjuster / ops review time 30–50%.
03
Sales & lead agents
Qualify inbound, enrich with public data, draft personalized openers, schedule meetings. Writes everything back to your CRM with confidence scores.
04
Internal-ops agents
HR queries, IT tickets, expense routing, knowledge-base lookups. Embedded in Slack or Teams; cuts ticket volume meaningfully without hiding behind 'contact support.'
05
Research agents
Take a topic, gather, synthesize, and produce a structured brief with citations. Like a tireless junior analyst that never plagiarizes.
06
Voice agents
Inbound and outbound voice — appointment reminders, post-service follow-up, opted-in customer engagement. Built on Vapi/Retell/Twilio with strict TCPA-aware compliance.
How we build
Five stages. No hand-waving.
01
Discovery sprint
1–2 weeks. Domain interviews, success metrics, throwaway prototype on the riskiest assumption.
02
Tool design
Map every API the agent will call. Each tool gets a typed schema, retry logic, and a permission boundary.
03
Build + eval harness
Senior engineers, 4–8 weeks. Eval harness shipped on day one — golden test sets running on every PR.
04
Hardening
Prompt-injection defense, cost caps, circuit breakers, observability dashboards. Production-grade or it doesn't ship.
05
Hand-off
Runbook, monitoring, on-call SLA. 30-day post-launch support window included.
What goes wrong (and what we do about it)
Four failure modes. Mitigations baked in.
- Tool errors. APIs time out, return malformed data, rate-limit. Mitigation: typed schemas, retry + circuit-breaker logic, dead-letter queues.
- Prompt injection. User-controlled text overrides the agent. Mitigation: input sanitization, privilege separation, output validation, an adversarial test suite that runs on every release.
- Cost spirals. Loops without termination conditions burn budget silently. Mitigation: per-session cost caps, max-step limits, monitoring on token spend per execution.
- Distribution shift. Inputs change post-launch and prompts drift out of alignment. Mitigation: weekly eval re-runs, input distribution monitoring, drift alerts.
Featured case study · Healthcare
Clinical document agent reduced doctor documentation time by 47%.
HIPAA-aligned AI agent processing clinical encounters into structured documentation. 6-week build, sub-3-second response time.
Read the full case study →Outcome
47%
reduction in documentation time per encounter
Industries we ship agents into
Featured agent case studies
Learn more about agents
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.
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.
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.