Resume
AI Agent Architect focused on LLM runtimes, multi-agent orchestration, and production AI infrastructure.
Summary
Agent systems engineer specializing in LLM runtimes, multi-agent orchestration, and production AI infrastructure. Creator of Haive, an open-source framework for adaptive multi-agent systems that move beyond fixed workflows by enabling agents to discover capabilities, compose tools, spawn sub-agents, and coordinate through stateful serializable graphs. Built 100+ agent implementations and 30+ open-source frameworks spanning agentic AI, quantitative systems, and developer tooling.
Professional Experience
Architected Haive's runtime and standardized ACP-style deployment services for dynamic multi-agent systems, with stateful serializable graphs, persistent memory, runtime reconfiguration, capability discovery, and MCP integrations.
- Built composable agent-team orchestration with planners, reasoners, memory, dynamic supervision, parallel execution, and validation and repair loops for adaptive workflows.
- Developed configurable RAG and context pipelines across 200+ sources, spanning custom ingestion, document processing, embedding, indexing and storage, retrieval orchestration, and 20+ RAG patterns.
- Designed evaluation and benchmarking systems across 22 game and application environments, using fixed-config ablations to compare tools, prompts, models, and agent architectures.
Led Corpay's agentic AI initiative by building multi-agent systems on Haive, including a KYC and AML watchlist assistant that achieved 96% detection accuracy, 5x faster throughput, and full coverage over sampling.
- Built a database intelligence agent and credit underwriting assistant for internal teams, improving data access and enabling faster, more consistent credit reviews.
- Engineered an automated forecasting and stress-testing platform using GARCH-based models for VaR, volatility, and scenario analysis, improving accuracy by 35% and reducing runtime by 75% across $500M.
- Built and maintained a centralized multi-repo platform, unifying CI/CD across credit, treasury, and trading.
Built and led a 10-person team developing EquityExpert, an early multi-agent platform for automated sell-side equity research with live, data-driven reports spanning fundamental, technical, macro, and quantitative analysis.
- Advanced to the Y Combinator interview stage for AI-driven analytics and document-ingestion systems.
Technical Skills
Selected Open-Source Platforms & Projects
Full portfolio, documentation, and package releases via willastley.dev , GitHub and PyPI.
Stateful graph runtime with serializable agents, tools, retrievers, and engines embedded in layered state schemas for reconfigurable, persistent, and reproducible execution.
Advanced orchestration layer for composing planner and reasoner teams with parallel DAG execution, validation and repair flows, MCTS-style reasoning, context management, and knowledge-graph and vector memory.
Ecosystem of 100+ standardized tools and MCP integrations, enabling agents to discover, retrieve, and extend capabilities with HITL-gated access across 2,000+ tools.
Agent and multi-agent benchmarking suite spanning 22 environments with fixed-config ablations over models, prompts, and architectures to capture reasoning, coordination, and robustness.
Deployment framework and ACP/MCP-style protocol serving agent graph workflows with JSON-RPC execution, lifecycle hooks, recovery, tracking, and Postgres-backed persistence.
50+ configurable agent presets and templates for common enterprise workflows, including support, research, content generation, code review, analytics, and domain-specific automation.
Open-source Perplexity-style research engine with quick search, pro search, and deep research implemented as LangGraph workflows for planning, retrieval, reflection, and synthesis.
Database intelligence agent using DB-RAG and long-term memory for schema-aware exploration, guarded query planning and execution, and iterative self-improvement.
Agentic OpenAPI-to-MCP system that transforms API specs into refined FastMCP and LangGraph server artifacts, improving tool docs and generating prompts, resources, templates, and server instructions.
Database-backed prompt registry and runtime delivery layer for LangChain and LangGraph with relational versioning, file-native prompt specs, and Postgres and MinIO-backed storage.
Multi-agent skills registry that ingests curated skills repositories, normalizes SKILL.md capabilities, and enables portable install, sync, and discovery across Codex, Claude Code, Cursor, and agent runtimes.
Unified LLM integration layer for LangChain-first applications with provider-aware model parsing, typed settings, live model discovery, LiteLLM metadata, and cost helpers.
Agent-native project generation framework for creating structured, reproducible coding environments with project context, tooling, commands, and conventions encoded up front.
Pydantic-based agent toolkit for structured, validated reasoning with logic ASTs, sequent-calculus proof validation, SAT checks, machine-checkable traces, and counterexamples.
Defensive prompt-injection guide and corpus with 200+ adversarial cases, attack taxonomy, detection rules, mapped defenses, and few-shot and evaluation integrations.
Early agent-style pipeline for converting handwritten notes, PDFs, and images into structured LaTeX through OpenCV preprocessing, OCR, LLM generation, and self-correction.
Unified quantitative and market-systems platform spanning time-series forecasting, stochastic and PDE simulation, technical analysis with 265 indicators, derivatives pricing, risk modeling, stress testing, backtesting, and agent-facing financial workflows.
Credit risk modeling pipeline for default probability estimation using logistic regression, KNN, and XGBoost with feature engineering and evaluation via ROC-AUC, Brier score, and calibration.
Education
Recruited into industry prior to degree completion based on technical achievement and open-source contributions.