How We Work

Your organization runs on knowledge that lives in people’s heads — the operations manager who knows which vendors are reliable, the compliance officer who knows the edge cases the policy manual missed, the sales engineer who knows which objections are real. This knowledge is your most valuable asset and your biggest bottleneck. Until it’s explicit, no agent can use it.

We spend 2–4 weeks inside your organization conducting structured knowledge extraction sessions with the people who know how things actually work. We don’t just document processes — we extract the decision heuristics, the edge cases, the judgment calls that make the difference between a process that works on paper and one that works in reality.

We translate everything into a knowledge architecture: structured specifications that are simultaneously human-readable and agent-legible. Decision frameworks become Given-When-Then scenarios. Heuristics become classification rules. Tribal knowledge becomes searchable, versioned, institutional intelligence.

• Artifact Knowledge Architecture — structured specifications, decision frameworks, and heuristics codified in both human-readable and agent-legible formats
• Artifact Workflow Decomposition — every process mapped on the Determinism Spectrum with recommended autonomy levels
• Artifact Specification Layer — Given-When-Then acceptance criteria for every automated workflow
• Artifact Institutional Knowledge Base — versioned documentation that serves onboarding, training, and agent development

Your team wrapped existing APIs in natural language descriptions and called them "agent tools." The demo worked. Production didn’t. Agents hallucinate file paths, call the wrong endpoints, and produce outputs that look plausible but miss what your users actually need. The gap between what an API does and what an agent needs is where all the engineering effort belongs.

We audit your existing tool surface — every API, CLI, MCP server, and integration your agents touch — and evaluate each one against our Three-Layer Tool Architecture.

The service layer contains your low-level system tools. The workflow layer sits above — your business logic. The domain layer is agent-specific: intention-shaped tools that encode what the agent is trying to accomplish. Instead of get_crm_records, your agent gets prepare_customer_meeting.

• Audit Tool Surface Assessment — every existing tool scored for agent-friendliness
• Design Three-Layer Tool Catalog — service, workflow, and domain tools mapped with contracts and examples
• Artifact Intention-Based Tool Specs — redesigned tool interfaces shaped around what agents are trying to accomplish
• System Tool Governance Framework — registry, naming conventions, versioning, and change management

You built one agent and it worked. Now everyone wants agents. But there’s no coherent platform — each team is building independently, using different patterns, with no shared infrastructure for memory, orchestration, evaluation, or governance. You need an architecture, not more prototypes.

We design the complete harness — the environment, tools, constraints, memory systems, skills, and evaluation infrastructure that sit between the LLM and your business processes.

The architecture includes context engineering: how context flows through the system — what lives in system prompts, what’s in the structured knowledge base, what’s retrieved via tools, and what agents build themselves through progressive disclosure.

• Blueprint Harness Architecture — complete technical design for orchestration, sub-agents, memory, context flow, and skill system
• Blueprint Governance Model — safe trajectory spaces, steward agent design, service integrity agreements
• Artifact Context Architecture — what goes where: system prompts, knowledge base structure, tool-retrieved context
• Ref impl Reference Implementation — a working proof of the architecture for your team to extend

Your agents respond when prompted. But the real leverage is in agents that act before anyone asks — monitoring event streams, detecting anomalies, maintaining knowledge, spawning investigations when they find something worth pursuing. The transition from request-driven to change-driven systems is where agents become genuinely transformative.

We identify the key events in your ecosystem and design agents that trigger on these events to take proactive action: suggest next steps, fetch context, auto-complete routine work, or spawn focused investigations.

We implement the Inception Pattern: scheduled agents with constrained self-spawning capability. The system grows when the world is interesting and stays quiet when it isn’t.

• Design Event Map — every key event in your ecosystem, what it means, and what agent action it should trigger
• Blueprint Spawn Architecture — the Inception Pattern with constraint sets: branching limits, temporal buffers, quality criteria
• System Prototype Ambient Agents — working agents for 2–3 priority use cases
• Blueprint Knowledge Graph Design — entity model, data sources, reconciliation logic, continuous refinement

You have agents in production. They seem to work. But when someone asks "how well are they performing?" or "why did it do that?", no one can answer. You have no way to tell the difference between an agent that’s doing excellent work and one that’s confidently producing garbage.

We instrument your agents to capture what matters: input context, tools chosen, reasoning traces, decisions made, and outcomes produced. We define evaluation scenarios tied to real business goals.

We implement the compound engineering loop: when an agent struggles or fails, we treat it as a signal — identify what’s missing and feed the fix back into the harness. Every mistake becomes a permanent improvement.

• System Observability Instrumentation — logging of input context, tool selection, reasoning traces, and outcomes
• Artifact Evaluation Suites — test scenarios defined by business goal with pass/fail criteria from your specifications
• Process Compound Loop — a systematic process for converting agent failures into harness improvements
• Recurring Agent Health Reports — monthly analysis of agent performance with prioritized recommendations

Your enterprise customers are starting to ask: "Can my agents use your product?" The honest answer is "kind of, but it’s janky." Your APIs were designed for human developers. Your documentation assumes a person is reading it. Your error messages are written for screens, not for reasoning models.

We audit your entire product surface from the perspective of an agent trying to accomplish a task. We apply the Agent Experience (AX) methodology: the same rigor your team brings to UX research, but for non-human users.

We redesign your tool interfaces to be intention-shaped — organized around what agents are trying to accomplish, not the internal structure of your system. We restructure documentation for retrieval and redesign error messages for agent self-recovery.

• Audit AX Assessment — every API endpoint, doc page, and error path scored for agent usability
• Design Intention-Shaped API Specs — redesigned interfaces organized around agent tasks
• Artifact Agent-Ready Documentation — restructured docs optimized for retrieval: chunked, schemaed, example-rich
• Guide AX Style Guide — ongoing standards for maintaining agent-friendliness as your product evolves