Division 02 · AI Systems Builder
Multi-agent orchestration platforms and autonomous research agents. We build systems that work in production — with safety layers, audit trails, and model routing that keeps cost manageable at scale.
How We Build
We start by mapping what you're actually trying to automate. Most people come in saying "we want an AI agent" but don't know which tasks, which tools, which data sources, or what the failure modes are. We interview your team, shadow your workflows, and write a scope document that defines: what the agent does, what it never does, and what triggers a human escalation.
We design the multi-agent structure: which tasks need specialized sub-agents, how agents communicate and hand off work, where memory and state live, and what the authority hierarchy looks like. A Council OS deployment has 7 functional groups (AI Ops, Technical, Business, Domain, Product, Operations, Security) with explicit escalation paths between them. No spaghetti orchestration.
Each agent gets the minimum tool set it needs to do its job — least-privilege applied to AI. We then design the model routing layer: simple classification to Haiku-class models, complex reasoning to Opus-class, code generation to specialist coding models. This reduces token cost 50–80% vs naive "send everything to the biggest model" architecture without quality loss.
We write the system prompts, orchestration layer, tool wrappers, and integration code. System prompts are treated as software — versioned, tested, and reviewed for injection surface. Tool wrappers enforce output validation so malformed external data can't hijack an agent's reasoning. Integration points (APIs, databases, file systems) are connected with auth on every call.
Before deployment, every agent system goes through a safety review against the OWASP LLM Top 10: prompt injection surface, excessive agency risk, data exfiltration paths, insecure output handling, and supply chain integrity of every dependency. We run adversarial tests — crafted external inputs designed to redirect agent behavior — and document what the system's trust boundaries are and why they hold.
We deploy with observability built in: every agent decision logged, every tool call recorded, every escalation timestamped. You get a dashboard of what the system did and why, not a black box. We also design the monitoring layer that watches for behavioral drift — when an agent starts acting outside expected patterns, it triggers an alert before it causes damage.
Active Systems
56-agent specialized workforce covering AI operations, security, business analysis, domain research, product design, and operations. Configurable for any research or business workflow. Running in production since 2025.
Autonomous research agent that ingests domains, keyword clusters, and research targets — then produces structured intelligence reports without human step-by-step direction.
Runtime monitoring agent that watches other agents for prompt injection patterns, behavioral drift, tool-use anomalies, and data exfiltration signals. Our internal security layer for agentic systems.
Task classification layer that routes agent workloads to the appropriate model tier — Haiku, Sonnet, or Opus — based on task complexity, latency requirements, and cost budget per operation.
What You Get
Get Started
Describe what you're trying to automate. We'll design a proposed agent architecture and walk through it on a call — no commitment required.
ai@bigheadinvestments.net · Long Beach, CA