AI Agent Development Studio

We have shipped AI agent systems into live products across DeFi, SaaS, and enterprise automation. Our stack centres on LangGraph for multi-agent orchestration, MCP for tool connectivity, and frontier models from Anthropic and OpenAI — selected per task based on latency, cost, and capability requirements.

A production agent is not a single prompt with a tool list. It is a network of nodes — orchestrator, executor, validator, memory — each with a defined role, bounded scope, and failure handling. We design agent architectures that are transparent, testable, and improvable over time.

What We Build

Multi-Agent Workflows: We design agent networks where each node has a clear responsibility. No monolithic prompts. No single point of failure. Orchestrators route tasks, executors act, validators confirm, and memory layers maintain context across sessions.

RAG Pipelines: Retrieval-augmented generation over your proprietary data. We handle chunking strategy, embedding model selection, vector store setup (Pinecone, Weaviate, pgvector), hybrid search, and retrieval quality tuning — so answers stay accurate as your corpus grows.

LLM Integration: Claude, GPT-4o, Gemini, and open-weight models (Llama 3, Mistral) are selected based on your latency and cost envelope. We handle prompt engineering, structured output schemas, tool definitions, and fallback routing.

Intent-Driven Dashboards: Agent interfaces that surface what the system is doing, why it decided what it decided, and what the user can override. Built for trust, not just throughput.

Evaluation & Observability: Evaluation harnesses using Braintrust and Langfuse. Regression suites that catch quality degradation before it reaches users. Tracing at every node so debugging takes minutes, not days.

On-Chain Agents: For DeFi-facing systems — MPC wallet integration for autonomous transaction signing, zkML-secured inference for privacy-preserving agent decisions, and Chainlink CCIP for cross-chain agent calls.

Who This Is For

DeFi protocols automating treasury management and risk monitoring. SaaS teams adding AI-powered workflows to existing products. Startups building AI-native tools from scratch — where the agent is the product, not a feature. Enterprise teams replacing manual, rules-based processes with adaptive agent systems.

Our Process

Discovery → Architecture Design → Spike & Prototype → Production Build → Evaluation Harness Setup → Deployment → Monitoring & Iteration. We scope in week one, spike in week two, and ship a working system — not a deck.