Architecting theAgent-Native Future
We are a collective of AI engineers building applications where agents are first-class citizens. We don't just consult; we co-build agent-native architectures ensuring parity, granularity, and composability.
Parity
Whatever the user can do through the UI, the agent must be able to achieve through tools. We build capability maps to ensure 1:1 coverage.
Granularity
We prefer atomic primitives over monolithic logic. Features become outcomes achieved by an agent in a loop using granular tools.
Composability
New features = new prompts. By building atomic tools, we enable agents to compose complex workflows without writing new code.
Emergent Capability
Build atomic tools and let agents solve problems you didn't explicitly design for. We optimize for the unexpected.
CRUD Completeness
For every entity, we verify the agent has full Create, Read, Update, and Delete capabilities.
Files as Universal Interface
Agents know how to manipulate files. We use file systems as the primary interface for agent-user collaboration.
Core Principles
A practical reference for building applications where agents are first-class citizens.
Input
Action
Result
1. Parity
Whatever the user can do through the UI, the agent must be able to achieve through tools. We build specific capability maps to ensure 1:1 coverage between user actions and agent methods.
2. Granularity
We prefer atomic primitives. Features are outcomes achieved by an agent in a loop. Instead of 'classify_and_organize_files', we give the agent 'read_file', 'move_file', and 'bash'.
3. Composability
New features = new prompts. When tools are atomic and parity exists, we can create complex workflows just by instructing the agent, without writing new code.
4. Emergent Capability
Agents can accomplish things you didn't explicitly design for. By building atomic tools, we empower agents to compose solutions for unexpected user requests.
Files as Universal Interface
Agents already know how to manipulate files using tools like grep, mv, and mkdir. We leverage the file system as the primary interface for agent-user collaboration, ensuring portability, inspectability, and a clear information architecture.
Structure
- {entity}.jsonEntity Data
- {type}.mdContent
- agent_log.mdAgent Logs
- {id}.checkpointCheckpoints
Context Pattern
Agents read a context.md at session start to understand who they are, what they know about the user, and what exists in the environment.
Documents/
├── AgentCheckpoints/ # Ephemeral state
│ ├── session_123.ckpt
│ └── session_124.ckpt
├── AgentLogs/ # Execution history
│ └── 2024-01-15.log
└── Research/ # Durable user work
├── project_alpha/
│ ├── context.md # Injection source
│ └── data.json
└── notes/
└── ideas.mdMethodology
We don't just write prompts. We build systems. Our methodology ensures robustness, scalability, and transparency.
Tool Design
Tools are the hands of the agent. We design them for parity and composability.
Context Injection
The prompt is the operating system. We structure context to prevent starvation.
Communication
Agents must be transparent. We implement strict event-driven communication protocols.
Completion Signals
We don't guess when the agent is done. We require explicit signals.
Common Anti-Patterns
Avoid these common pitfalls when building agentic systems.
Services
From architecture to production, we help you build applications where agents are first-class citizens.
Strategic Architecture
We design the agent loop, capability maps, and data strategy to ensure your system is ready for agentic interaction.
Tool Implementation
We build robust, atomic tools in TypeScript or Python that connect your business logic to agent reasoning.
Legacy Transformation
We refactor monolithic features into granular primitives, making your existing platform agent-ready.
Production Delivery
We handle the critical infrastructure: state management, checkpoints, eval pipelines, and observability.
Get in Touch
Ready to build agent-native? Send us a message and we'll get back to you shortly.