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AI Agents Development
We build AI agents you can deploy to production - not demos you'll rewrite six months
Since 2013, we delivered
over 300 projects for 23 markets
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AI Tools we use at Empat
Case study AI
See how Empat delivers production AI agents through real business workflows — built with the architecture, tool integrations, and edge-case coverage that separates production systems from demos.
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HealUa
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The HealUA app facilitates Ukrainian doctors in seeking expert assistance from colleagues worldwide.
We cover every detail of your entire project for success
Custom AI development services for production-grade software products with scalable architecture, launch readiness, and strong technical oversight.
AI MVP development services for rapid startup validation, clear deliverables, and GPT-powered workflows — built as a fast fixed-scope MVP.
You vibecoded an app but it doesn’t work as it should? We can help.
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What's Included in Our AI Agent Engineering
Agent Discovery & Architecture
Before any code is written, we map the business workflow, the data sources, the tool integrations, the failure tolerance, and what "done" looks like in production. Most agents fail not because of the model but because the architecture was designed for a demo, not for real users and real load.
Customer Support Resolution Agents
We build support agents that handle real customer interactions — returns, billing disputes, account questions, order lookups — using MCP-powered tool access to take actual actions, not just generate answers. Built with clear escalation logic so a human steps in when the agent shouldn't proceed alone. Target: 80%+ first-contact resolution with the right cases still reaching your team.
Knowledge & RAG Agents
We build retrieval-augmented generation systems that surface organizational knowledge on demand — internal documentation, product databases, policies, case history — with source attribution, role-based access control, and auditability for regulated environments. Knowledge agents that answer from your data, not from a model's assumptions.
Sales Pipeline & Lead Intelligence Agents
We build agents that research prospects, monitor accounts for buying signals, qualify leads across firmographic and technographic dimensions, and prepare per-lead context for outreach — reducing SDR research time and improving message relevance at scale. Our delivery includes ICP/IPP profiling, account intelligence, and outbound personalization across channels.
QA, Testing & Production Readiness
Every agent we ship goes through structured testing: adversarial inputs, tool failure scenarios, ambiguous user queries, latency under load, and escalation coverage. We test the failure modes that kill agents in production — not just the happy path. Accessibility and auditability checks are part of our release criteria.
Monitoring, Iteration & Long-term Support
Agents degrade as the world changes — data drifts, APIs change, user behavior evolves. We build observability into every deployment and help teams improve agent performance based on real usage data, not guesses. Your team can take these handoffs straight to delivery.
How We Deliver AI Agents for Real Business Operations
At Empat, AI agent delivery is not about shipping features faster — it is about shipping the right ones. Most demos never reach production. We build differently: senior engineers own architecture, tool integration logic, and every release-critical decision. AI accelerates what should be accelerated. Humans control what shouldn't be delegated.
We start with the problem, not the model. Before choosing tools or architecture, we map the business workflow, the failure tolerance, the data sources, and what "good" looks like in a real production environment.
We define the agent's scope, tools, and escalation logic. We map which actions the agent can take autonomously, which require confirmation, and which should always escalate — because getting this wrong is what makes agents unsafe to deploy.
We design the architecture for long-term use. Tool use, memory strategy, context management, fallback behavior, and monitoring hooks — all planned before any code is written.
We build the core agent system. Our team develops the agent logic, connects MCP tools and external integrations, covers the edge cases, and builds the product-facing interfaces needed for reliable delivery.
Senior engineers stay in control. Architecture, business logic, QA, and release-critical decisions remain under experienced human oversight. Every line of AI-generated code is read and approved by a human before it ships.
We test against production conditions — adversarial inputs, API failures, load scenarios, ambiguous user queries. We test what breaks agents, not just what makes them work in a clean environment.
After launch, we help teams improve and scale. We iterate based on real usage data, coverage gaps, tool performance, and the edge cases that only appear when real users interact with the system.
Ready to Build AI Agents That Hold Up in Production?
Tell us the problem you're solving — customer support volume, internal knowledge access, lead research, workflow automation. We'll review the workflow, map the architecture, and propose a delivery path that ships to production, not to a demo environment. Book an AI Discovery Sprint: 3 days, scoped output, clear next step.
Our expertise amplifies them
Healthcare
Agents that handle returns, billing disputes, subscription questions, order lookups, and account management — using MCP tools to take real actions, not just generate text. Built with clear escalation paths so humans handle the cases that require judgment, and the agent handles everything it reliably can.
Education
Retrieval-augmented assistants that give teams instant access to internal docs, policies, case history, and product knowledge — with source attribution, role-based access control, and the auditability that regulated industries require. Answers from your data, not from the model's training.
Fintech
Agents that research prospects, monitor accounts for buying signals, qualify leads, and prepare per-lead context for outbound outreach — reducing the research burden on SDRs and improving personalization at scale. Connected to LinkedIn, Crunchbase, Apollo, and other real-time data sources.
Retail
Custom agents connected to Slack, Jira, HubSpot, Salesforce, and internal systems — replacing repetitive manual operations with reliable automated workflows that integrate with the tools your team already uses, without rebuilding everything from scratch.
FAQ
What AI agent development services does Empat provide?
Empat builds production AI agents for customer support automation, knowledge management and RAG systems, sales intelligence and pipeline automation, and workflow automation connected to your existing tools via MCP integrations.
What makes an AI agent "production-ready" vs. a prototype?
Production readiness means tested edge cases, real tool integrations, fallback and escalation logic, behavior under load, and monitoring from day one — not just a demo that works once. We treat architecture, testing, and failure modes as delivery-critical from the start.
Can Empat act as an AI agent development partner for startups and product teams?
Yes. We work with product teams that need to move from idea to production without the "vibecoded demo that breaks after launch" cycle. We scope clearly, test the failure modes, and own delivery.
Do you build agents that integrate with our existing tools?
Yes. We use MCP (Model Context Protocol) to connect agents to CRMs, support platforms, databases, Slack, Jira, HubSpot, Salesforce, and custom internal systems — so agents take real actions, not just generate text responses.
What kinds of problems are AI agents actually reliable for right now?
Customer support with structured resolution flows, knowledge retrieval over internal documentation, lead research and account monitoring, and repetitive operational workflows where the business logic is clear and consistent. We're direct about where agents still fail — we won't pitch autonomous decision-making for problems that genuinely need human judgment.
How do you handle escalation and human oversight in support agents?
Every agent we build has explicit escalation paths — cases where the agent recognizes it shouldn't proceed alone and hands off to a human. For support agents, this is defined by action risk level, confidence threshold, and case type. Escalation logic is part of the spec, not an afterthought.
Can you stabilize an existing AI agent that isn't working reliably in production?
Yes. If an agent was built quickly and is failing — wrong tool calls, hallucinated responses, poor coverage, no fallback logic — we can audit the architecture, identify the failure modes, and improve the system incrementally without a full rewrite.
How do we get started?
The first step is a short discovery conversation. We review the workflow you want to automate, the tools involved, the tolerance for errors, and what success looks like in production — then we recommend a scoped path forward.
Read our blog
Empat Earns Claude Certified Architect Certification: What It Means for Our Clients
We're proud to announce that our team has earned the Claude Certified Architect (CCA) certification from Anthropic. It’s one of the most exclusive and technically demanding AI certifications currently available in the market.
