Agentic software development is the practice of building products where part of the process is dynamically directed by an LLM — within a deterministic architecture and explicit trust boundaries, not a free-for-all. The hard part is rarely the model; it is the harness, the context engineering and the eval discipline around it. We work to our own openly published Agentic Product Standard, so every agent we ship has earned its autonomy, has a measurable Definition of Done, and keeps working when the model underneath it changes.
End-to-end agentic engineering, grounded in the Agentic Product Standard and proven in production.
We map your problem onto the autonomy ladder, decide single- vs multi-agent, and choose the composition pattern — so freedom is earned and bounded, not assumed.
The 7 layers around the LLM loop — routing, validation, guardrails, retries, observability — where reliability actually lives.
Well-scoped, least-privilege tools exposed over the Model Context Protocol, with sandboxing and fail-closed auth.
Auditable, citation-enforced retrieval and memory — including AgenticMind, our open-source knowledge & memory layer on Postgres + pgvector.
An eval pyramid, judge calibration and trace review, so we measure before we ship and improve from real behaviour, not vibes.
Workflow + activity patterns for long-running, recoverable agent processes that don’t lose state when something fails.
An iterative, eval-first process: we earn autonomy step by step, instrument everything, and harden before we hand over.
We start from the problem, not the model: what must be deterministic, where an LLM genuinely adds value, and how much autonomy each step has earned. The output is a clear agent contract, trust boundaries and a measurable Definition of Done.
We design the deterministic architecture and the 7-layer harness around the LLM loop — routing, validation, guardrails, retries and observability — choosing single- vs multi-agent and the right composition pattern.
We implement the agent, wire up well-scoped tools over MCP, and connect an auditable memory & knowledge layer. Context engineering and citation-enforced retrieval keep answers grounded and current.
We build the eval pyramid, calibrate judges and review traces, then close the loop on cost, latency and failure modes — so the system is production-ready, observable and recoverable before launch.
We partner with global brands and fast-growing innovators to deliver software that works
Voicer is a customer feedback management system that assists businesses in coping with the growing number of customer reviews. With Voicer, you can collect and organize reviews in one place, analyze data to discover trends, and use that information to improve products and services.
Djooky is a platform that enables music lovers to support the artists they love. Our team has been working on this project since early 2020, and we've already seen amazing results: 200,000 users from over 140 countries and a lot of positive feedback from users.
To help you with any questions that are not listed here, we offer free, no-obligation consultations
A product where part of the process is dynamically directed by an LLM inside a deterministic architecture with explicit trust boundaries — not a chatbot bolted onto a UI. Autonomy is earned per step and always bounded.
We are framework-agnostic and pick per task: Claude, OpenAI and others for models; LangGraph, the Claude Agent SDK or OpenAI Agents SDK for orchestration. Architecture beats framework, so the design outlives the tools.
98% of reliability lives in the harness around the model. We invest in the 7-layer harness, context engineering and eval-driven development, with trace review and a 12-point production-readiness audit before launch.
Yes — our own openly published Agentic Product Standard (MIT). Every engagement is designed, built and reviewed against it, so quality is consistent and auditable.
Absolutely. We integrate agents, tools (MCP) and a memory/knowledge layer into existing systems, with guardrails and evals so the new autonomy is safe and measurable.
Book a free consultation below. We’ll scope the problem, map it onto the autonomy ladder, and propose an architecture and a Definition of Done.
More from our agentic practice — the standard we build to, the open-source layer that implements it, and the team that ships it.
Ready to build an agentic product that survives production? Let’s talk.