Agentic systems
Building multi-agent systems with Google ADK and the Claude SDK, then integrating them into enterprise platforms.
AI Engineer · Dallas, TX
Aidan Marshall
I build agentic AI systems, the production tooling around them, and the controls that let enterprise teams use them safely.
I take agentic systems from prototype to production inside large, regulated organizations. My work spans legal, tax, and compliance platforms where useful automation still needs clear evidence, cost controls, and human judgment.
Currently Senior AI Engineer at PwC · Previously IBM
Enterprise AI delivery
Shipping AI into legal, tax, and compliance environments where governance, cost, and reliability shape the design.
AI-assisted development
Building workflows around Cursor, Claude, and Copilot that shorten implementation and make review easier to measure.
Experience
Where I've done the work
- Architected and scaled agentic AI systems with Google ADK and the Claude SDK, integrating autonomous workflows into a globally distributed enterprise platform generating over $5B in annual revenue.
- Led the 0-to-1 integration of Harvey AI into the core product, owning end-to-end system design and the data pipelines behind specialized legal and compliance capabilities.
- Designed a composite-weight system that compares real-time agentic compute cost with output and developer productivity.
- Introduced AI-assisted development workflows with Cursor, Claude, and Copilot that shortened implementation across the team.
- Delivered enterprise-grade agentic AI for a Fortune-level tax enterprise, building multi-agent workflows on the Microsoft Agent Framework with Azure OpenAI and Microsoft Foundry, orchestrated over Model Context Protocol (MCP) servers under strict governance and compliance standards.
- Built client-facing agentic AI prototypes used in engagements and proofs of concept for IBM and Neudesic.
- Tested agent patterns, tool integrations, and custom state and orchestration flows before client adoption.
Selected projects
Selected Work
Three public projects with code, limitations, and enough evidence to inspect the implementation.
Open source · Python
Agent Autonomy Score
A CLI that scores implementation intent and code diffs, then recommends how closely a human should supervise the agent.
Open source · LLM routing
LLM Bidding System
An auction router where coding models bid on work and a supervised workflow records which model succeeded at what cost and risk level.
Open source · Python
Carry Trade Research Model
A finance-origin research system with FX collection, model experiments, a Flask API, and a React dashboard that labels missing and derived data honestly.
Other projects
- Event-Driven Multi-Agent ArchitectureArchitecture study · Multi-agent
A design study for running multi-agent AI systems on an event-driven service bus: agents subscribe to and emit events instead of calling each other directly, so they can be added, upgraded, or hot-swapped live without redeploying the rest of the system. Covers the event envelope, agent registry, reliability, and LLM cost on transports like Azure Service Bus and Kafka.
- Macro Oil Desk TerminalFull-stack · Azure AI Foundry
A trading-desk terminal for an oil desk — Brent/WTI dislocation z-scores, US inventory drawdown velocity, and AIS-based tanker fleet exposure by regulatory regime — with an Azure OpenAI and Microsoft Foundry-backed market-commentary panel and a real-time 3D Earth visualization. Built on Next.js and FastAPI, deployed on Azure.
- Autonomous Native AI Mobile Navigation AgentClaude Code · iOS
An iOS app for the Newport Beach Boat Show with an embedded multimodal assistant that interprets user intent and autonomously navigates the app's interface.
Writing
Technical notes from the work
Short, first-party notes on agent systems, reliability, and model economics.
The agentic future still runs on distributed systems
AI lowers the barrier to persistent, adaptive automation—and raises the reliability demands placed on the systems underneath it.
Read article →Multi-agent autonomy is compositional
A multi-agent system creates risks through the sequence of actions its participants can take together.
Read article →Model capability is converging. Model economics are not.
One real coding task cost roughly 80 times more with one frontier model than another, with negligible difference in the final result.
Read article →Capabilities
What I work with
Contact
Let's build something.
I'm open to conversations about agentic AI, enterprise automation, and engineering teams. LinkedIn is the best way to reach me.