AI Agent Development
We build AI agents that work in production. Not just in demos.
AI agents have been over-hyped and under-delivered. Most demos are chatbots wearing a costume. We build agents that execute real workflows, make real decisions within defined boundaries, and handle what happens when things go wrong. Built into your product or your operations, designed to ship.
320+ products delivered. We’ve shipped software in industries where broken isn’t an option. We bring that standard to every agent we build.
What most teams get wrong about AI agents
There is a gap between what AI agents are marketed as and what most of them actually do in production.
An agent that works in a demo has a clean input, a well-scoped task, and no edge cases. An agent that works in production has to handle ambiguous inputs, incomplete data, conflicting instructions, API timeouts, and users who ask things you didn’t plan for. It has to know when to act and when to ask. It has to fail gracefully when it’s wrong instead of silently causing downstream damage.
Most teams underestimate this gap. They build something impressive in a prototype and discover the production problem later. The escalation logic wasn’t designed. The failure modes weren’t tested. The decision boundaries weren’t defined. The monitoring isn’t there.
We’ve shipped enough real software to know what production looks like. We build agents for that environment, not for the demo.
What an AI agent actually is
An agent acts and completes. A chatbot only responds.
An AI agent is a system that takes a goal or a task, reasons through the steps needed to complete it, uses tools and external systems to gather information and take actions, and produces a result — with minimal human input at each step.
That’s meaningfully different from a chatbot, which responds to input but doesn’t execute steps. It’s different from RPA (robotic process automation), which follows fixed scripts but can’t handle variability. An agent combines language model reasoning with the ability to use tools, call APIs, read from and write to systems, and make decisions about what to do next.
What makes it hard is the decision layer. The agent has to know what it’s allowed to do. It has to know when it’s confident enough to act autonomously. It has to know when to pause and escalate to a human. Getting those boundaries right is most of the engineering work — and most of what separates production agents from demo agents.
What we build
Seven kinds of agents, all built for production.
Customer-Facing AI Agents
Intelligent agents embedded in your product that your users interact with directly. These include support agents that resolve issues without human intervention for the cases they handle confidently, copilots that assist users through complex workflows, onboarding agents that guide new users through product setup, and AI assistants that know your product’s context and your user’s history. These are product features, built with the same care as anything else in your product.
Workflow Automation Agents
Agents that handle the judgment-heavy steps inside your business or product workflows. Not rule-based scripts that break when the input varies. AI-driven decision steps that route incoming requests, classify documents and data, draft outputs for human review, validate information against defined criteria, and flag exceptions that need human attention. The agent handles the volume; humans handle the edge cases.
Internal Knowledge Agents
Agents that answer questions from your own documentation, knowledge base, internal systems, or product data. Connected to your actual information through a retrieval pipeline, not dependent on general training data. Useful for support teams, sales teams, internal onboarding, and any context where your people need to quickly find answers that live in your own systems.
Document Intelligence Agents
Agents that process documents at scale: read, extract, classify, summarize, and route. Useful in workflows that currently depend on manual document review — contracts, intake forms, patient records, property documents, financial filings. The agent produces structured outputs your systems can act on, with human review built into the workflow where the stakes require it.
Agentic Features in SaaS Products
AI agent capabilities built directly into the product you sell to your customers. Autonomous task completion triggered by user actions, multi-step background workflows that run without constant user prompts, intelligent scheduling and recommendation systems that act on behalf of users. This is where GTC’s product engineering background matters most: the agentic feature has to fit cleanly into the product architecture and the user experience.
Multi-Agent Workflows
For complex tasks that benefit from specialization, we build systems where multiple agents work together. One agent extracts, another validates, another routes, another notifies. Each agent is scoped precisely to what it can do reliably. The orchestration layer manages the handoffs. The overall system accomplishes something more complex than any single agent could handle cleanly.
Agentic Features in SaaS Products
After an agent is live, you need to understand what it’s doing. We build monitoring and governance into every deployment: decision logs, action audit trails, escalation rate tracking, cost monitoring, and performance dashboards. These are not optional extras. They are how you build confidence in the agent over time, catch problems before they compound, and improve the agent’s behavior based on real usage data. Agents we build are designed to be observable and governable by your team, not opaque systems you have to trust blindly.
How we approach agent development
Six steps, and the first one matters most.
AI features behave differently at scale with real-world data. Our process is built to close the gap between a prototype and an integration that works reliably in production.
Step 1: Define the agent's job precisely
Before any code, we define exactly what the agent does. What are the inputs? What are the outputs? What decisions does it make autonomously? Where does it stop and wait for human input? What systems does it read from and write to? The biggest failure mode in agent projects is scope that’s too broad. We insist on precision here before we start.
Step 2: Design the decision architecture
We design the agent’s decision logic: which model powers which decisions, how confidence thresholds trigger escalation, what the tool catalog looks like and what permissions each tool carries, how memory and context are managed across sessions, and what the full failure-state map looks like. This is the work most teams skip in prototypes and pay for in production.
Step 3: Build and integrate
We build the agent, connect it to your systems and data, and develop the full integration layer. This includes the orchestration logic, the retrieval pipeline if retrieval is needed, the tool integrations, the human escalation paths, and the API layer that connects the agent to your product or your operations.
Step 4: Test against real inputs and failure modes
We test with representative real-world inputs, not the clean examples that worked in development. We probe the failure modes: what happens when the API the agent depends on is down, when the user input is ambiguous, when the model produces a low-confidence output, when the document the agent reads is malformed. We tune the decision boundaries until the behavior is reliable enough to ship.
Step 5: Deploy with observability built in
We deploy with monitoring from day one: decision logs, action audit trails, cost tracking, latency monitoring, escalation rate tracking. Not as an afterthought. These aren’t just useful for debugging — they’re how you understand what the agent is doing in production and where to improve it.
Step 6: Iterate after launch
Agents tuned on test data behave differently with real users. We stay involved after launch to review what’s happening in production, adjust decision boundaries, improve prompts and retrieval, and tune the escalation logic based on what the real data is telling us.
What every production agent needs
The five things demo agents skip.
Defined decision boundaries
The agent must know what it is and isn’t allowed to do without human approval. These boundaries are designed upfront, not discovered after the agent does something unexpected.
Human-in-the-loop escalation
For every decision type, we define what confidence threshold triggers autonomous action and what triggers a handoff to a human. The handoff path has to be as well-designed as the autonomous path.
Failure-state handling
When an API the agent depends on fails, when the model produces a low-confidence answer, when the input doesn’t match any expected pattern — the agent needs a defined behavior for each of these. Failing gracefully is a design requirement, not a nice-to-have.
Audit trails and observability
You need to know what the agent decided, why it made that decision, and what actions it took. This matters for debugging, for compliance in regulated industries, and for building confidence in the system over time.
Cost management
LLM calls are priced by token. In high-volume agent workflows, costs compound fast. We design cost-aware architectures: caching, appropriate model selection per task, token budget controls, and cost monitoring after launch.
Technology we build with
The right tooling sized to the problem.
Language models and providers
OpenAI (GPT-4 and o-series models), Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral) for use cases where data privacy or cost requirements make them the right choice. We choose based on task requirements, not defaults.
Orchestration frameworks
Lang Chain, Lang Graph, Llama Index, Crew AI, and Auto Gen. We choose the right framework for the agent’s complexity. Simple single-task agents don’t need heavy orchestration. Complex multi-agent workflows need proper state management. We size the tooling to the problem.
Memory and retrieval
Vector databases (Pinecone, Weaviate, pgvector), document stores, and RAG pipeline design. Persistent memory management across sessions where the agent needs to remember context from prior interactions.
Tool integrations
REST APIs, databases, CRM and ERP systems, communication platforms (Slack, email), internal knowledge bases, and any other external system the agent needs to read from or write to. We build clean, permissioned tool catalogs with proper access controls.
Cloud and infrastructure
AWS Bedrock, Azure AI Agent Service, Google Vertex AI, and custom deployment on any major cloud provider. Infrastructure aligned with your existing environment and security requirements.
Application and product layer
React, Angular, Node, Java, React Native, iOS, and Android. Agents built to fit cleanly into your existing product architecture, not as separate systems bolted on afterward.
Industries where we build agents
Production agents, domain by domain.
Real estate and proptech.
Document extraction agents for transaction workflows, intelligent routing agents for lead management, property data extraction from unstructured sources, and AI-assisted compliance checks for transaction documents. We understand the data complexity and regulatory sensitivity in this space.
Healthcare
Patient intake and triage agents, appointment coordination workflows, clinical document processing, and AI-assisted routing for provider systems. Built with data privacy and regulatory requirements as baseline constraints. Human oversight built into every patient-facing decision path.
Education platforms.
Intelligent tutoring agents, student support and guidance bots, administrative workflow automation for enrollment and scheduling, and AI-powered feedback agents for learning platforms. From early-stage edtech to institutional platforms.
Enterprise SaaS.
Customer support agents that handle volume without sacrificing quality, internal knowledge assistants connected to company documentation, intelligent ticket routing and classification, and AI copilots embedded in line-of-business products.
Marketplace platforms.
Listing quality agents that review and improve submitted content, intelligent matching agents for two-sided workflows, automated categorization and tagging, and buyer-seller communication agents with escalation to human support.
Why product engineers build better agents
Building an agent that works in production is a product engineering problem. Not just AI Problem
The agent has to fit into your existing system architecture. Its API integrations have to be designed with the same discipline as any production API. Its failure modes have to be handled with the same rigor as any production service. Its user-facing behavior has to be designed with the same care as any product feature. And the codebase has to be maintainable by your team after we’ve built it.
Teams that specialize only in AI often miss the product engineering layer. They build impressive models and fragile systems. The integration breaks. The UX is an afterthought. The codebase is difficult to maintain.
We’re a product engineering team. We’ve shipped production software for twelve years across industries where reliability matters. We bring that discipline to every agent we build. The AI layer is our capability. The product engineering layer is our foundation.
What we don't do
We don't train custom agent foundation models. If your use case requires a custom model rather than integration with existing AI providers, we'll tell you that and help you find the right team.
We don't manage post-launch model retraining infrastructure. We build agents with stable, well-engineered integration to existing providers. Monitoring and prompt tuning post-launch, yes. MLOps and model infrastructure, no.
We don't build agents for use cases where the risk profile requires enterprise-grade governance infrastructure we don't have. If you're in a heavily regulated industry and need formal AI governance frameworks, that's a different engagement. We'll tell you if your situation falls into that category.
"We needed AI search built into our platform without rebuilding the whole product. GTC designed the integration cleanly, it shipped on time, and it actually improved how users found things. That was the measure that mattered."
"They were honest from the start about what AI would and wouldn't solve for our specific product. That scoped the project correctly from day one. The integration worked in production on the first try."
FAQ
Questions teams ask before building an agent.
A chatbot responds to conversation. It takes input, generates a response, and that’s the transaction. An RPA (robotic process automation) system follows a fixed script: if X, do Y. It can’t handle variation outside what it was programmed for. An AI agent does something different: it takes a goal, reasons through the steps to achieve it, uses tools and external systems to gather information and take actions, and makes decisions along the way. An agent can handle variation, interpret ambiguity, and execute multi-step workflows that a chatbot or RPA script can’t. The technical capability is real. The challenge is building it so that it works reliably when the inputs are messy and the stakes are real.
We build customer-facing agents embedded in products, workflow automation agents for business processes, internal knowledge retrieval agents, document intelligence agents, and multi-agent systems for complex workflows. Which type fits your situation depends on what problem you’re solving and where the agent lives. The best starting point is a clear description of the specific task you want the agent to handle. If you can describe the inputs, the expected outputs, and the decisions the agent would need to make, we can tell you which approach fits.
This is one of the most important design questions in any agent project, and we address it before we write code. We define decision categories: which actions the agent is allowed to take without human approval, which actions require human confirmation before execution, and which situations the agent should hand off entirely. These thresholds are based on consequence severity, confidence levels, and the cost of a mistake. They’re not one-size-fits-all. A support routing decision has a different risk profile than a document approval action. We define the boundaries explicitly and test against them.
We work with OpenAI, Anthropic Claude, Google Gemini, and open-source models where the use case calls for them. For orchestration, we use LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen depending on the complexity of the agent workflow. We don’t have a preferred vendor or framework. We choose based on what fits the use case, the cost profile, and the data privacy requirements. We also design with model-agnosticism in mind: the agent architecture shouldn’t break if you need to swap the underlying model later.
A focused single-task agent typically runs four to eight weeks from scoping to production deployment. Multi-agent systems or agents with complex system integrations take longer. The most significant variable is your data and system readiness: if the APIs the agent needs to call exist and are documented, and the data the agent needs to retrieve is accessible and well-structured, things move fast. If those foundations need work first, we factor that into the timeline.
We design for this before we ship. Every agent has defined failure states: what happens when the model output doesn’t meet the confidence threshold, when an external API call fails, when the input doesn’t match any expected pattern. These aren’t handled by hoping the model gets it right. They’re explicit code paths with defined behavior. The typical design is: the agent logs the failure, takes the conservative action (which often means escalating to a human), and produces an audit trail of what happened and why.
You do. Full source code, all prompt configurations, all integration logic. From day one. The agent runs on your infrastructure or your choice of cloud provider. There is no dependency on a proprietary platform or on us to keep it running. We design with vendor independence in mind at the model level too: we build abstraction layers so that swapping the underlying AI provider doesn’t require rewriting the agent.
No. Most of our clients come in with a clear business problem and limited or no internal AI experience. What we need from you is access to the right context: the people who understand your workflows, your data, and your systems. We handle the AI and engineering. You handle the domain knowledge and the decisions about what the agent should and shouldn’t do. The engagements that work best have a clear product or operations owner on your side who can make decisions quickly, not a team that already knows how to build agents.
They complement each other rather than replace each other. RPA is well-suited for high-volume, predictable, rules-based tasks: exactly repeatable steps with structured inputs and no variability. AI agents handle the tasks where the input varies, the steps require judgment, or the rules don’t cover every case. A common pattern is: RPA handles the structured processing layer, and an AI agent handles the exception cases and the judgment calls that the RPA workflow can’t process. We can build agents designed to work within an existing automation stack rather than replacing it.
The main drivers are: the complexity of the task the agent needs to perform, the number of external systems it needs to integrate with, how much data preparation and structuring work is needed before the agent can function, and whether human-in-the-loop escalation paths require custom UI work. A focused single-workflow agent with clean system integrations is the least expensive scenario. A multi-agent system with complex data retrieval, multiple API integrations, and custom oversight tooling is significantly more. We scope every project specifically and give you a clear cost estimate before you commit. We don’t do open-ended engagements where the cost is determined after work starts.
The key requirements are: the systems the agent needs to read from or write to need to have accessible APIs or data exports, and the data the agent needs to use needs to be reasonably well-structured. We do an integration assessment before scoping the project. The most common blocker is that internal systems the agent needs to access don’t have clean APIs, or data is fragmented across systems in ways that complicate retrieval. We identify these gaps in the assessment phase so they’re not surprises during the build.
Agents behave differently with real users than they did in testing. We expect this and plan for it. After launch, we monitor decision patterns, escalation rates, cost usage, and output quality. We use this data to tune prompts, adjust decision thresholds, and improve retrieval quality. We stay involved for a defined post-launch period and offer ongoing engagement for iteration as your use cases evolve. We don’t disappear after deployment.
Ready to integrate AI into your product properly?
Ready to build an agent that actually works?
Tell us what workflow or use case you have in mind. We’ll walk through what the agent would actually need to do, what the technical approach looks like, and what it would take to build it properly.
Thirty minutes. A senior engineer. A straight answer on what’s feasible and what isn’t.
No pitch. No proposal until we understand what you’re trying to automate.