Build AI Agents That Reach Production
MetaSys designs, builds, and operates production-grade AI agents for enterprise operations across logistics, fintech, healthcare, and SaaS. First agent in 2 weeks.
The gap between a working demo and a production AI agent.
Most AI agent projects stall between prototype and production. The demo works. The production deployment does not. Four patterns explain almost every failure.
Built for demos, not workflows
Most prototypes are scoped around a single happy path. Real workflows have exceptions, retries, and edge cases. Agents built without those constraints fail the moment real data arrives.
No evaluation wired in
Teams ship an agent without measuring accuracy, latency, or drift. There is no way to know if it is working correctly or degrading, so confidence collapses and it gets quietly turned off.
Integration depth underestimated
Connecting to a live ERP, CRM, or data warehouse is harder than connecting to a sandbox. API rate limits, auth token rotation, schema drift, and data quality issues sink agents that looked fine in staging.
Human-in-the-loop added too late
Production requires approval gates, escalation paths, and audit trails. Adding these after the agent is built means structural rework, not configuration.
MetaSys structures every engagement to resolve these before they occur. Read how our agentic systems work.
Six types of production AI agents.
Every agent we build is scoped to a specific workflow, evaluated on real data, and deployed with full observability. These are the agent categories we ship most often.
Exception handling agents
Detect operational exceptions across your ERP, WMS, or CRM and resolve them autonomously. Classifies root cause, drafts responses, notifies stakeholders, and logs every decision.
Logistics, Finance, OpsDocument processing agents
Extract, classify, validate, and route documents from any source. Clinical records, contracts, invoices, compliance filings. Structured output, traceable decisions, no human review queue required.
Healthcare, Legal, FinanceCustomer and sales agents
Handle inbound qualification, answer product questions, update CRM records, and escalate to humans when judgement is needed. Runs 24/7 across channels without supervision.
SaaS, Retail, EnterpriseData monitoring agents
Watch your data pipelines, dashboards, and KPIs for anomalies. Alert the right person, trigger the right workflow, and log every decision with full traceability.
Data, Finance, OperationsMulti-agent orchestration
Some problems require specialist agents working in sequence or in parallel. We architect topologies where agents hand off to each other with shared memory and state, producing compound outputs no single agent could.
Enterprise, Complex workflowsCompliance and audit agents
Run automated compliance checks, generate audit trails, flag policy violations, and produce regulatory reports without manual review cycles. Built for regulated sectors from day one.
Healthcare, Fintech, EnterpriseNot sure which type fits your problem? Book a scoping call and we will map the right architecture to your workflow.
The process behind every production deployment.
We follow the same four-phase process on every engagement. It is designed to surface the problems that kill agents in production before they become expensive.
Scope
We map the workflow the agent will own. Inputs, decisions, actions, escalation paths, and evaluation criteria. We define measurable success before writing any code.
Architect
We design the agent topology: model selection, tool integrations, memory architecture, retrieval layer, and human-in-the-loop gates where the workflow requires them.
Build and evaluate
We build against real data in a staging environment. Evaluation is wired in from the first build. Accuracy, latency, and edge-case handling are measured before anything ships.
Deploy and operate
We deploy to production with observability, automated alerting, and a retraining pipeline. Managed operations are available if you want the system monitored and improved over time.
MetaSys is headquartered in Missouri with offices in the UK and Pakistan. Every United States engagement runs in US time zones with a dedicated delivery lead available during your business hours. Our practices are SOC 2-aligned and HIPAA-ready, and we build with CCPA-awareness for any system that handles personal data. We serve companies across the United States, from early-stage SaaS teams to enterprise operations groups.
What separates our agents from the rest.
Evaluation built in from day one
Every agent we build has accuracy, latency, and cost tracked from the first iteration. Quality is measured, not assumed.
Production, not prototypes
We build systems that ship and keep running. Observability, alerting, and rollback are part of every deployment, not optional extras.
You own the IP
The code, models, and infrastructure are yours. No lock-in, no black box, no proprietary runtime you cannot inspect or move.
Senior engineers lead the work
Experienced AI engineers scope, architect, and build. We do not staff your project with juniors learning on your budget.
"MetaSys did not just build what we described. They asked the right questions up front, spotted three edge cases we had missed, and shipped a system that actually runs in production. The accuracy held up on real data from day one."
Zika
GMetrics, Germany
AI agent development: what clients ask before starting.
How much does AI agent development cost?
Scoped engagements typically start from $25,000 for a single-agent production deployment. Multi-agent systems and ongoing managed operations are priced based on complexity, data volume, and the number of integrations. We provide a fixed-fee proposal after a scoping call.
How long does it take to build an AI agent?
Most clients have their first production agent live within 2 weeks of starting the build phase. Complex multi-agent systems with deep integrations typically take 6 to 10 weeks end to end. We can confirm a timeline after scoping.
Do we own the IP for the agents you build?
Yes. The code, models, pipelines, and infrastructure are yours. We do not retain any rights to the systems we build for you, and there is no vendor lock-in.
What models and frameworks do you use?
We select models based on the task: GPT-4o, Claude 3.5 Sonnet, Llama 3, and fine-tuned domain models where needed. For agent frameworks we use LangGraph for stateful flows, LangChain for tool integration, and CrewAI for multi-agent orchestration. We can also build custom agent runners for specific production environments.
How do you handle accuracy and reliability in production?
We wire evaluation in from the first build. Every agent has accuracy, latency, and drift tracking from day one, with automated regression testing and production dashboards with alerts. Across our deployed agents, we maintain 94%+ average decision accuracy.
How do we get started?
Book a 30-minute scoping call with an AI Architect. Bring a workflow or problem you want to automate. Most clients hear back within one business day.
Have a question we have not answered? Ask our team directly.
Ready to ship your first production agent?
Bring a workflow you want to automate. Walk away from the first call with a scoped architecture and a clear path forward.
30-minute call, no commitment. Most clients hear back within one business day.