Agentic systems engineered for production, not prototypes.
Production agentic systems require deliberate design pattern choice, a context layer that governs intent and guardrails, and the observability and cost discipline that make multi-agent workflows operable at volume. We engineer all of it, in environments where every decision has consequence.
A Marketing Agent that turns an inbound RFP into a structured pursuit recommendation.
What we typically see
Most agentic AI initiatives stall not at the model layer, but at the system layer. The prototype optimizes for capability. The production environment demands reliability across thousands of decisions a day, none of which can be rolled back.
The production gap
At twelve months in, the prototype performs and the rollout has not shipped. Operating reliably under uncertainty is a different build.
Architecture without intent
Orchestrator, pipeline, event-driven, single agent. Each pattern fails differently. Teams that inherit one by accident inherit failure modes they did not plan for.
Cost as a limiting reagent
Multi-agent loops compound token consumption exponentially. Without cost discipline engineered in from the outset, return erodes before adoption builds.
Agents without a context layer
A capable model without a governed context layer is a fast, confident, and uninformed system. Its decisions cannot be defended, audited, or reversed.
How we work
We architect and deploy agentic systems as part of an Artificial Intelligence engagement. Infrastructure, agent development, and production deployment are scoped iteratively, with one priority agent validated in production within the first sixty days.
01
Containerized runtime, CI/CD, and observability infrastructure
02
Design pattern selection, autonomy framework, and context layer integration
03
API and workflow integrations into your data layer and operational tools
04
Architecture progression from Claude Organizational Skills and MCP to AWS Bedrock as scale requires
The output is production-deployed agents your team can operate and extend, with the runtime, integration patterns, and operational playbooks that make every subsequent build faster to ship.
"LLMs can work around edge cases and apply ad hoc logic to complete tasks that would have been impossible with regular software."
James Townend
Across industries and teams
Marketing Agents
Built for Marketing and business development teams operating proposal generation, RFP analysis, and content production workflows
Higher Education Agents
Built for Higher Education teams handling enrollment, financial aid, advising, and student services at scale
Healthcare & Life Sciences Agents
Built for Healthcare and life sciences teams running prior authorization, intake, care coordination, and clinical operations
Manufacturing Agents
Built for Manufacturing teams running sourcing intelligence, document automation, and predictive maintenance triage
From the field
Innovative thinking. Real outcomes.
Saving 4-6 hours per proposal with a marketing AI copilot
Encoding 40+ years of expertise into an AI knowledge assistant
The Production Gap: Why Your AI Agent Needs a Micromanager
OneSix Named a Cortex Code Preferred Partner
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Questions we hear
Why do most agentic AI pilots stall in production?
The shift from a prototype that worked once to a system that works a thousand times a day across teams is a different engineering problem. Production agentic systems are distributed, stateful, and operating under uncertainty. They require design pattern choice, context layer design, evaluation framework, observability, and cost discipline the prototype never needed. Closing that gap is the engagement.
What is the typical starting point for an engagement?
Most agentic AI engagements begin with a sixty-day scoping and build phase. We deliver one priority agent in production and establish the patterns subsequent builds will follow.
What is a context layer and why does an agent need one?
A context layer encodes the business meaning around your data: who is asking, what they are allowed to see, what has happened in this relationship before, and what the operational rules are. Without it, a capable model becomes a fast and confident system that cannot defend the decisions it makes.
How do you keep agents accountable in production?
Through evaluation framework design, per-request traceability that reconstructs the agent's path and reasoning, behavioral drift monitoring, escalation paths for low-confidence decisions, and human-in-the-loop checkpoints engineered at the architecture layer. Governance is engineered into the architecture from scoping forward.
How do you choose an agent design pattern?
Pattern choice is a function of workload characteristics, not architecture preference. Fixed sequential processes call for pipeline patterns. Dynamic routing calls for orchestrator patterns. High-volume, decoupled responses call for event-driven patterns. Narrow, bounded tasks can be served by a single agent. Most production systems blend patterns. We design for the failure modes the workload actually has.