From pilot to production
Agent Engineering
Take AI agents from a promising pilot to production traffic your customers can rely on.
What is agent engineering?
An agent that impresses in a demo has to cope with live customers, inconsistent data and edge cases that no one scripted. Agent engineering is the discipline of building AI agents that keep working once they meet those conditions. Only 17% of organizations have deployed AI agents to date, while more than 60% expect to within two years, according to the 2026 Gartner CIO and Technology Executive Survey. The gap between a demo and a production-level agent is where most of those programs will be won or lost.
QKTech treats an agent the way it treats any production system. We build it through a defined lifecycle, assemble it from reusable and custom components, and release it with the rigor applied to any other software product. Clients who want our engineers inside their own team can start with AI Forward Deployed Engineers, and every agent we ship is held to the checks in Reliability of AI.
Insights
What our agents deliver
100,000+
Agent hours saved each year after an electric mobility company moved call summarization to a production AI agent, with 90% faster call disposition
80%
Faster reporting turnaround for a consumer goods company’s field sales teams, using a conversational Data Analyst Agent
25+
Production-ready connectors delivered for DevRev’s AI-powered Snap-Ins marketplace, with 50% less development effort
150,000+
Employees given governed GenAI access at a global manufacturing conglomerate
How we engineer agents for production
The practice breaks into five areas, each covering a different part of how an agent gets built and run.
Agent-led Development Lifecycle
Agents built one-off by different teams behave differently and are hard to govern. One disciplined path from agent scope to production release, covering design, build, evaluation and deployment. Context, harness, loop and trust engineering are set during the build, because those choices shape behavior in production more than testing added at the end.
Pre-packaged Agents
Rebuilding the same agent for every team wastes months. Ready-to-deploy agents, including Marqk agents for industries and business functions, cover common enterprise workflows and are configured to your environment and data.
Custom Agents
Some workflows are too specific for anything off the shelf. We engineer purpose-built agents end to end for that workflow or system.
Product Engineering
An agent that passes in a demo can fail with real customers. Product engineering builds the agent as a product, with the testing, versioning and release practices that let it act consistently across customers, tenants and versions.
Platform Engineering
Agents need controlled access to your tools, or one wrong call can reach systems it should never touch. Platform engineering builds the infrastructure an agent runs on, covering orchestration, tool access and the guardrails that keep it inside its intended scope.
Partners
Technology Partners
We build agents on the platforms our clients already run, from service desks to automation suites.
QKTech was the first major digital assurance provider to go AI-native with Computer by DevRev, and built DevRev’s AI-powered Snap-Ins marketplace with 25+ production connectors.
Named International Agentic Testing Pioneer in the UiPath 2026 Partner Awards for agent-led testing on the UiPath platform.
FAQs
Common questions about agent engineering
What is agent engineering?
Agent engineering is the work of designing, building and operating AI agents that hold up in production, covering the development lifecycle, the platforms agents run on and the automation around them.
Is agent engineering the same as agentic AI?
Agentic AI describes AI that can act on its own. Agent engineering is the discipline of building that capability reliably, from the first scoping decision through release and ongoing operation.
How do you take an agent from pilot to production?
We rebuild the pilot through our development lifecycle, add evaluation and guardrails, connect it to the systems it depends on and release it with a rollback path.
Do you offer ready-built agents?
Yes. Pre-packaged agents cover common enterprise workflows and are configured to your data and systems, with custom agents built where nothing off the shelf fits.
What are context, harness, loop and trust engineering?
Context engineering decides what an agent sees at each step, including retrieval sources and permission-aware data. Harness engineering builds the environment around the model, from tool access to recovery when an upstream system fails. Loop engineering designs the cycle that verifies each result and decides when to retry, stop or hand the work to a person. Trust engineering builds the controls that let a risk team and a regulator rely on the agent, including audit trails and human approval points.
Talk to our agent engineering team
Tell us about the agent you want to build, or the pilot that now has to run in front of customers. We will come back with a view on what it needs to reach production and how we would engineer it.