Agentic Engineering
AI agents that do real work, reliably, in production.
LLM-powered features, AI agents and automated workflows, engineered with evaluations, guardrails and cost control.
The problem
Why this is hard
Getting an impressive AI demo takes an afternoon. Getting an agent that works on the thousandth request, handles bad input gracefully, stays within budget and never takes an action it should not takes engineering.
That is the work we do. We treat models as powerful but unreliable components and build the system around them: well-designed tools and context, evaluations that measure quality, guardrails and human approval where the stakes are high, and the observability to know what the agent actually did.
Capabilities
What we do
AI use-case discovery
Find where agents create measurable value in your business, and where a simpler solution is better.
LLM features in your product
Search, summarization, extraction, drafting and assistants built into your applications with good UX and clear failure modes.
Agents & tool use
Agents that use your APIs, databases and internal tools to complete multi-step tasks, with human approval where it matters.
Retrieval & knowledge systems
Grounding models in your own documents and data with retrieval pipelines that are measured for accuracy, not just demoed.
Evaluations, guardrails & observability
Test suites for model behavior, safety checks, tracing and cost monitoring, so you can ship changes with confidence.
AI-assisted engineering
Help your engineering team adopt coding agents safely, with specs, review gates, CI and workflows that actually raise output.
Engagements
Typical ways to start
2–3 weeks
AI opportunity sprint
Workshops, a feasibility spike on your real data, and a prioritized roadmap with effort and risk estimates.
6–12 weeks
Agent build
Design, build and deploy a production agent or LLM feature, with evaluations and monitoring from day one.
4–8 weeks
Engineering team enablement
Set up agentic development workflows for your team, including tooling, guardrails, review practices and training.
What you get
- Production agent or AI feature, deployed in your environment
- Evaluation suite with baseline quality metrics
- Guardrails, permissions and human-in-the-loop design
- Tracing, cost and quality dashboards
- Documentation and operating playbook
Technologies we use
Chosen for your context, never for our convenience.
- Claude
- OpenAI
- Open-weight models
- Model Context Protocol (MCP)
- Agent SDKs
- Python
- TypeScript
- Vector search / pgvector
- LangGraph
- Evaluation frameworks
- OpenTelemetry
FAQ
Agentic Engineering questions
What is agentic engineering?
It is the discipline of building software systems where AI models plan and take actions, like calling APIs, querying data and completing multi-step tasks, not just generate text. It also covers using coding agents to build software faster. In both cases the hard part is not the model. It is the engineering around it: context, tools, evaluation, safety and operations.
Which models do you use?
We are model-agnostic and pick based on task quality, latency, cost and data requirements. We design systems so that switching models later is straightforward.
Is our data safe?
We design for your compliance requirements from the start. That covers enterprise API agreements with no training on your data, private deployments where needed, least-privilege tool access, and full audit logging of agent actions.
How do you know an agent is good enough to ship?
We build an evaluation set from real examples before we build the agent, and we track it on every change. Shipping is a decision based on measured quality against agreed thresholds, not on a good-looking demo.
Related services
Have something that needs building?
Tell us where you are and where you need to be. You will talk to an engineer, not a salesperson, and get a straight answer on whether we are the right fit.