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AI AGENT DEVELOPMENT

AI agent development

We build agents connected to your systems to reduce repetitive support, document and data work. We define data access and human approval steps upfront.

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On this page4 sections
  1. The agents we build
  2. How we work
  3. Security, human approval and runtime
  4. Tooling

We start by reviewing the work your team repeats, and together we choose the steps an agent will take over. Then we connect the application to your existing workflow. Before release we test response quality, and in production we monitor latency and cost.

The agents we build

  • Support agents that classify incoming requests, open records in the target system and write outcomes back to the user
  • Knowledge assistants that answer from enterprise documents and cite the source behind each answer
  • Development agents that generate tests, upgrade dependencies and prepare migration steps in your codebase
  • Analytics agents that query data, summarize reports and explain deviations

How we work

  1. Scope and permissions

    We define upfront which requests the agent handles, its read and write permissions per system, and the steps that require human approval.

  2. Evaluation set

    We build an evaluation set from real requests and accepted answers in your organization and test every change against it.

  3. Least-privilege integration

    Every tool gets a dedicated service account scoped only to the endpoints it needs.

  4. Shadow mode and LLMOps

    We validate decisions in shadow mode before release and track prompt versions, evaluation runs and production traces in one LLMOps pipeline.

Security, human approval and runtime

High-impact actions such as payments, customer messages, data deletion and permission changes require human approval, with the underlying sources shown to the reviewer. Secrets stay in a vault and every action leaves an audit trail. When using provider APIs we configure data retention and log redaction; when data must stay on-premises, we run open-source models on your Kubernetes cluster with vLLM or Ray Serve behind Apache APISIX.

Tooling

Foundation models (chosen per task)
ClaudeGPTOpen-source options
Agent frameworks
Claude Agent SDKOpenAI Agents SDKLangGraph
Enterprise retrieval (RAG)
Postgres pgvectorQdrantOpenSearchSearch and reranking
Evaluation and observability
Eval setsLangfuseOpenTelemetry GenAI
Runtime and access
KubernetesApache APISIX (token authentication)vLLM (in-house serving)Ray Serve (in-house serving)

Let's talk.

Discuss your needs in a 30-minute call.

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Other services

LLM platform
Model access, RAG and evaluation for answers grounded in enterprise data. Data boundaries and usage costs are part of the design.
MLOps platform
ML infrastructure that lets data science and platform teams track model versions, releases and production behavior together.
Agentic modernization
LLM agents prepare repetitive code changes; code review and tests verify each change. Our team remains responsible for architectural decisions.
Application modernization
Backed by our App Modernization on Microsoft Azure specialization, we modernize legacy .NET and Java applications in stages, protecting working business processes with tests.