AI systems that plan, act, and operate without human loops
We build autonomous AI agents and multi-agent systems that execute complex, multi-step workflows — not chatbots, not wrappers. Purpose-built agentic infrastructure that integrates with your existing systems, operates within your compliance framework, and replaces entire categories of human operational overhead.
The Problem We Solve
The enterprise AI market has bifurcated into two failure modes. The first: vendors selling chatbot interfaces as "AI transformation" — systems that answer questions but do not act on them. The second: research-grade multi-agent frameworks that require a PhD team to operate and collapse outside the demo environment. Neither produces the outcome an enterprise buyer actually needs — AI that can take a workflow end-to-end, handle exceptions intelligently, and operate without a human supervising every step.
Agentic AI is not the same engineering problem as AI platform engineering. A model that generates good outputs is table stakes. An agent that reliably executes a multi-step workflow — querying external APIs, making decisions based on retrieved context, handling failure states, escalating when appropriate, logging every action for audit — requires a fundamentally different architecture. Most AI teams build for the happy path. We build for the operational reality: ambiguous inputs, unavailable tools, conflicting data sources, and compliance requirements that constrain what actions the agent is permitted to take.
The automation-versus-agent distinction matters more than vendors admit. n8n, Zapier, and Make.com are workflow automation tools — they execute predefined paths when predefined conditions are met. Agents are different: they reason about what path to take, select tools dynamically, handle conditions that were not predefined, and can operate on goals rather than triggers. An automation tool can send an email when a form is submitted. An agent can receive an unstructured request, determine what data it needs, retrieve it from three different systems, synthesize a response, determine whether human review is required based on confidence thresholds, and either act or escalate — all without a predefined workflow.
The compliance dimension of agentic AI is where most deployments fail in regulated industries. An agent that can take actions — write to databases, call external APIs, send communications — has a much larger compliance surface than a model that only generates text. Every action must be logged with sufficient detail to reconstruct what the agent did and why. Guardrails must prevent the agent from taking actions outside its authorized scope. In healthcare, an agent cannot retrieve PHI it is not authorized to access, regardless of what the user asks for. In financial services, an agent cannot provide advice that triggers licensing requirements. These constraints must be built into the agent architecture — not added as a prompt instruction.
First call is with a senior engineer. No sales rep. No pitch deck. We tell you honestly whether we can help.
Talk to an Engineer →Industries We Serve This In
How Our Teams Approach This Differently
We scope every agentic engagement around a specific operational outcome — not around the technology. Before writing code, we map the workflow the agent will replace: every decision point, every data source, every exception path, every compliance constraint. The agent architecture emerges from this map. The tools, the memory strategy, the orchestration pattern, the escalation logic — everything is derived from the operational reality, not from a framework preference.
Our agents are built for production operations, not for demos. This means we engineer for failure: what does the agent do when an API is unavailable? When retrieved context is ambiguous or contradictory? When the user request falls outside the agent's authorized scope? We build and test failure handling before we demo success paths. By the time an agent goes to production, it has been tested against hundreds of adversarial inputs designed to expose edge cases.
Compliance-aware agent guardrails are not prompt engineering. A HIPAA-aware agent does not have instructions in its system prompt telling it not to share PHI. It has architectural controls: a retrieval layer that enforces data access permissions before returning context, an action validation layer that checks every tool call against a compliance ruleset before execution, and an audit logging layer that records every action with the data that would satisfy a regulatory inquiry. ALICE enforces compliance at the agent infrastructure level — not the instruction level.
What You Get
A production-deployed agentic system scoped to a specific operational workflow — not a proof of concept, not a demo agent. The agent is tested against realistic operational conditions including failure states, adversarial inputs, and compliance edge cases before go-live.
Full LLM-Ops infrastructure: model selection and evaluation framework, performance monitoring against task-specific metrics, drift detection, guardrail enforcement, and cost optimization. The agent does not degrade silently — the monitoring infrastructure catches performance regression and alerts before it affects operations.
Compliance documentation appropriate to your regulatory environment. Every agent action is logged with audit-ready detail. If the agent operates in a HIPAA environment, the logging satisfies HIPAA audit requirements. If it operates in a financial services context, the decision trail satisfies the documentation requirements of your jurisdiction's regulator.
Source code, architecture documentation, and runbooks transferred at engagement close. Your team receives documentation sufficient to extend the agent's capabilities, add new tools, and modify escalation logic without requiring us to return. The system continues operating. You own it completely.
How Our Engineers Deliver This
Our agentic AI teams build systems that operate without human intervention loops — not demonstrations or prototypes. An agent we deploy for a healthcare client can triage inbound clinical requests, pull relevant patient history, cross-reference formulary data, and generate a compliant draft response — within HIPAA guardrails, with every action logged. The agent does not call a human for each step. It operates. We build the agent, the compliance layer, the monitoring, and the escalation logic. Then we leave. The system keeps running.
Engagement Models
Where We Deploy
Build vs. Outsource Decision Framework
A structured framework — with scoring — for deciding whether to build in-house, outsource, or adopt a hybrid model. Adapted for regulated industries where the cost of the wrong decision is highest.