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(00)initmode · agent architecture · zürich — remote

Your agents need
architecture.

Prototypes demo well. They rarely survive compliance reviews, cost audits, or their first thousand users. We architect agent systems — from retrieval pipelines to guardrails — for the constraints that kill them in production.

init mode

load constraints …ok

map data flows …ok

design topology …ok

harden · ship

(01)serviceswhat we deliver

What we deliver

01

Agentic AI Architecture

We architect agent systems that actually ship — not stall at the prototype. From scoping to production deployment.

On-Premise. Cloud. Hybrid.

02

Multi-Agent Systems

Complex problems need more than one agent. We design how they divide work, share context, and keep moving when something breaks.

Task Decomposition. Routing. Failure Recovery.

03

RAG & Knowledge Architecture

Agents are only as good as what they know. We build the pipelines that feed them the right information at the right time.

Ingestion. Vector Search. Retrieval.

04

Security & Guardrails

Your agents handle sensitive data, make decisions, and talk to external systems. We make sure they can't be tricked, leaked, or hijacked.

Prompt Injection. Data Exfiltration. Privilege Escalation.

05

Cost Architecture

Agent costs spiral fast without the right architecture. Model routing, caching, and right-sized infrastructure so your agents stay fast without burning your margin.

Token Optimization. Model Routing. Infra Sizing.

(02)processhow we work

How we work

(01)

Discovery

Define constraints, map data flows, audit knowledge sources, and identify every integration surface.

(02)

Design

Architect agent topology, retrieval pipelines, tool schemas, guardrails, and observability — on paper before code.

(03)

Build

Implement with LangGraph, CrewAI, AutoGen, or custom orchestration. Your stack, our patterns.

(04)

Harden

Security audit, cost profiling, load testing. Production handoff with documentation.

1990s — shell scripts2010s — CI/CD pipelinesnow — agent systems

(03)machine roommulti-agent topology

Multi-agent collaboration

step 01

One supervisor

A single orchestrator owns the task — decomposing work and dispatching it to the specialists.

step 02

Three specialists

Planning, execution, and reflection. The reflector closes the loop, feeding evaluations back to the supervisor so the system corrects itself before you have to.

step 03

Shared services

Specialists draw on shared retrieval, memory, tools, and guardrails through a common service bus.

SUPERVISOROrchestratorPLANNERTask decompositionEXECUTORAction dispatchTOOL USEAPI / DB / SearchGUARDRAILSSafety / Compliance
fig. 01 — multi-agent topology · supervisor / specialists / shared services

(—)stack.lock

OpenAIAnthropicLangChainLangGraphCrewAIAutoGenPineconeWeaviateChromaQdrantRedisAWS BedrockAzure AIGoogle Vertex

(04)philosophyhow we think

Most agent builds fail at the architecture layer.

We are not a dev shop. We are an architecture practice. Every engagement starts with constraints — infrastructure limits, compliance requirements, latency budgets, cost ceilings — and with your data: what knowledge exists, where it lives, how agents will retrieve it. Before a single agent is scoped.

Every system we design ships with trace-level logging, cost-per-invocation dashboards, and security guardrails tested against OWASP LLM Top 10. No retrofitting. No technical debt by default.

We believe the best agent systems are precisely scoped, rigorously tested, and simple enough to explain in a single diagram.

(05)contactbook a scoping call

Let's scope
your architecture.

Ready to build something real? Tell us about your project and we'll get back to you within 24 hours.

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Switzerland — Remote
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