AWS Re:cap 01: Coinbase re:Invent Recap (IND3312)

Video: https://www.youtube.com/watch?v=IkK6dSsC5fU

Scaling support, compliance, and productivity with conversational

Overview of Gen AI Development in Financial Services

2023: Introduction of LLMs

● LLMs for customer and enterprise applications began to enter the market.

● Initial focus on trust and security of data.

● Questions about data safety and utility arose.

● Importance of Retrieval Augmented Generation (RAG) for businesses.

● FSI customers had valuable data but were uncertain about safe utilization.

Customer Support Chatbot

● Over 50% of customers prefer chat as their support channel.

● Trained on Coinbase's extensive knowledge base.

● Provides instant, accurate assistance around the clock, adapting to market volatility.

FAQ Style RAG Responses

● Simple, FAQ-style responses to automate common queries (e.g., sign-in issues, two-factor authentication).

● Focuses on accuracy to build a strong foundation.

Cohere's Rerank model enhances search relevance

● Semantically reorders initial search results

● Understands query-document context beyond keywords

● Supports over 100 languages

● Handles complex data like JSON and code

● Improves Retrieval-Augmented Generation (RAG) systems

● Increases enterprise search accuracy

● Provides faster user experiences

● Functions as a powerful second stage in search pipelines

● Sorts results from keyword or vector search by deep relevance scores

● Key Features

● Rerank-v3.5: state-of-the-art performance, enhanced reasoning, and broad data compatibility (text, JSON, code).

● Rerank-english-v3.0: English documents and semi-structured data.

● Rerank-multilingual-v3.0: For non-English content, supporting over 100 languages.

● Rerank 3 Nimble: Prioritize efficiency and lower latency for high-volume use cases like e-commerce search, improving conversion rates.

Cross-encoder architecture

● A model that processes two inputs (like a query and a document) jointly in a single sequence, using cross-attention mechanisms between all tokens to determine their relevance.

● Key Characteristics

● Joint Processing

● Cross-Attention

● Single Output

● First Stage (Bi-encoder)

● Second Stage (Cross-encoder)

Compliance Processes

● Implementation of KYC, KYB, and TMS.

● Heavy reliance on human resources and thorough investigations.

● Need for detailed explainability and adaptability to diverse regulatory requirements across countries.

● Strategy for Compliance

● Leverage of the internal platform for accessing LLMs and data through MCP interfaces.

● Inclusion of a traditional machine learning model to detect high-risk compliance cases, built on the AnyScale-based ML platform.

Ray-powered Scalability

● Optimizes and manages Ray clusters.

● Efficient distribution and scaling across thousands

● of CPUs and GPUs.

● Handles massive datasets and complex models.

● Ray AI Libraries

● Ray Data: For scalable data processing, particularly suited for unstructured data like images, audio, and video.

● Ray Train: For distributed model training, with integrations for frameworks like

● PyTorch, PyTorch Lightning, and Hugging Face Transformers.

● Ray Tune: A framework for scalable and fault-tolerant hyperparameter tuning and experiment execution.

● Ray Serve: For deploying and serving machine learning models as scalable, programmable APIs with low latency.

● RLlib: An industry-grade library for reinforcement learning, supporting a wide range of applications from robotics to finance.

Compliance Auto Resolution Engine (CAR)

● Central to the solution, acting as the orchestrator for the holistic review process.

● Mobilizes both automation and human expertise in an agentic AI-driven workflow.

● Human-in-the-Loop Process

● Interaction with two human personas:

● Compliance Operations Agents: Review AI system findings and provide feedback for improvement.

● End Customers: Reach out for additional information when necessary.

Coinbase re:Invent recap (IND3312)

Scaling support, compliance, and productivity with conversational AI at Coinbase

Customer Support Chatbot

● Over 50% of customers prefer chat as their support channel.

● Trained on Coinbase's extensive knowledge base.

● Provides instant, accurate assistance around the clock, adapting to market volatility.

FAQ Style RAG Responses

● Simple, FAQ-style responses to automate common queries (e.g., sign-in issues, two-factor

● Focuses on accuracy to build a strong foundation authentication).

Cohere's Rerank model enhances search relevance

● Semantically reorders initial search results

● Understands query-document context beyond keywords

● Supports over 100 languages

● Handles complex data like JSON and code

● Improves Retrieval-Augmented Generation (RAG) systems

● Increases enterprise search accuracy

● Provides faster user experiences

● Functions as a powerful second stage in search pipelines

● Sorts results from keyword or vector search by deep relevance scores

Cohere's Rerank model Key Features

● Rerank-v3.5: state-of-the-art performance, enhanced reasoning, and broad data

● Rerank-english-v3.0: English documents and semi-structured data. compatibility (text, JSON, code).

● Rerank-multilingual-v3.0: For non-English content, supporting over 100 languages.- Rerank 3 Nimble: Prioritize efficiency and lower latency for high-volume use cases like e-commerce search, improving conversion rates.

Cross-encoder architecture

A model that processes two inputs (like a query and a document) jointly in a single

sequence, using cross-attention mechanisms between all tokens to determine their relevance.

Key Characteristics

● Joint Processing

● Cross-Attention

● Single Output

● First Stage (Bi-encoder)

● Second Stage (Cross-encoder)

Compliance Processes

● Implementation of KYC, KYB, and TMS.

● Heavy reliance on human resources and thorough investigations.

● Need for detailed explainability and adaptability to diverse regulatory requirements across

countries.

🛠Strategy for Compliance

● Leverage of the internal platform for accessing LLMs and data through MCP interfaces.

● Inclusion of a traditional machine learning model to detect high-risk compliance cases, built

on the AnyScale-based ML platform.

Ray-powered Scalability

● Optimizes and manages Ray clusters.

● Efficient distribution and scaling across thousands

● of CPUs and GPUs.

● Handles massive datasets and complex models.

Ray AI Libraries

● Ray Data: For scalable data processing, particularly suited for unstructured data like images,

● Ray Train: For distributed model training, with integrations for frameworks like PyTorch,

audio, and video.

● Ray Tune: A framework for scalable and fault-tolerant hyperparameter tuning and

PyTorch Lightning, and Hugging Face Transformers.

● Ray Serve: For deploying and serving machine learning models as scalable, programmable

experiment execution.

● RLlib: An industry-grade library for reinforcement learning, supporting a wide range of

APIs with low latency.

applications from robotics to finance.

Compliance Auto Resolution Engine (CAR)

● Central to the solution, acting as the orchestrator for the holistic review process.

● Mobilizes both automation and human expertise in an agentic AI-driven workflow.

Human-in-the-Loop Process

Interaction with two human personas

● Compliance Operations Agents: Review AI system findings and provide feedback for

● End Customers: Reach out for additional information when necessary improvement.

Coinbase re:Invent Recap (IND3312)

Scaling Support, Compliance, and Productivity with Conversational AI

Customer Support Chatbot

● Over 50% of customers prefer chat as their support channel.

● Trained on Coinbase's extensive knowledge base.

● Provides instant, accurate assistance around the clock while adapting to market volatility.

FAQ-Style RAG Responses

● Simple FAQ-style responses automate common queries, such as sign-in issues and two-factor authentication.

● Focuses on accuracy to build a strong foundation.

Cohere's Rerank Model Improves Search Relevance

● Semantically reorders initial search results.

● Understands query-document context beyond keywords.

● Supports more than 100 languages.

● Handles complex data such as JSON and code.

● Improves Retrieval-Augmented Generation (RAG) systems.

● Increases enterprise search accuracy.

● Provides faster user experiences.

● Functions as a powerful second stage in search pipelines.

● Sorts keyword or vector search results by deep relevance scores.

Key Features of Cohere's Rerank Model

● Rerank-v3.5: State-of-the-art performance, enhanced reasoning, and broad data compatibility across text, JSON, and code.

● Rerank-english-v3.0: For English documents and semi-structured data.

● Rerank-multilingual-v3.0: For non-English content, supporting more than 100 languages.

● Rerank 3 Nimble: Prioritizes efficiency and lower latency for high-volume use cases such as e-commerce search, improving conversion rates.

Cross-Encoder Architecture

A model that jointly processes two inputs, such as a query and a document, in a single sequence and uses cross-attention across all tokens to determine their relevance.

Key Characteristics

● Joint processing

● Cross-attention

● Single output

● First stage (bi-encoder)

● Second stage (cross-encoder)

Compliance Processes

● Implements KYC, KYB, and TMS.

● Relies heavily on human resources and thorough investigations.

● Requires detailed explainability and the ability to adapt to different regulatory requirements across countries.

🛠 Compliance Strategy

● Uses the internal platform to access LLMs and data through MCP interfaces.

● Includes a traditional machine learning model, built on the AnyScale-based ML platform, to detect high-risk compliance cases.

Ray-Powered Scalability

● Optimizes and manages Ray clusters.

● Efficiently distributes and scales workloads across thousands

● of CPUs and GPUs.

● Handles massive datasets and complex models.

Ray AI Libraries

● Ray Data: For scalable data processing, especially unstructured data such as images, audio, and video.

● Ray Train: For distributed model training, with integrations for PyTorch, PyTorch Lightning, Hugging Face Transformers, and other frameworks.

● Ray Tune: A scalable, fault-tolerant framework for hyperparameter tuning and experiment execution.

● Ray Serve: Deploys and serves machine learning models as scalable, programmable, low-latency APIs.

● RLlib: An industry-grade reinforcement learning library supporting applications from robotics to finance.

Compliance Auto Resolution Engine (CAR)

● Central to the solution, acting as the orchestrator for the holistic review process.

● Mobilizes automation and human expertise in an agentic AI-driven workflow.

👥 Human-in-the-Loop Process

Interaction with Two Human Roles

● Compliance operations agents: Review AI system findings and provide feedback for improvement.

● End customers: Are contacted for additional information when necessary.

2024: Adoption and Scaling

● Release of Amazon Bedrock and other solutions for secure data access and management.

● Customers began using AI in key areas like customer support.

● Focus shifted to scale, capacity, and granular security.

● Exploration of applying well-architected frameworks to AI for future growth.

● 2025: Advanced AI Applications

● Emergence of multi-agent autonomous applications.

● Customers aim to transform core business operations and departments.

● Emphasis on measurable impact and business KPIs.

● Shift from singular tools to revolutionizing customer and employee workflows.

AWS Services for Gen AI

AWS Offerings

● Pre-built agents for quick deployment.

● Experimentation with open-source tools.

● Building a custom agent.

● Provision of models, tools, infrastructure, and expertise.

● AI Infrastructure and Tools

● Robust AI infrastructure, custom silicon, and data foundation tools.

● Decades of experience in secure, resilient, and globally scaled infrastructure.

● Advanced controls for data privacy, access management, monitoring, and observability.

● AWS Transform

● Automation of legacy workload modernization (e.g., .NET frameworks, VMware).

● Agents in Amazon Connect and tools in the marketplace.

● Investment in interfaces, protocols, security, and network enhancements for AI.

Amazon Bedrock

Service Overview

● Fully managed service for building, deploying, and operating Gen AI applications.

● Access to leading foundation models (Anthropic, Meta, Mistral, Amazon) via a single API.

● Continuous addition of new models.

● Customization and Safety

● Tools for privately customizing models and applications with data.

● Application of safety guardrails, cost optimization, and latency reduction.

● Rapid iteration capabilities.

● AgentCore:

● Set of services for secure, scalable agent deployment and operation.

● Serverless nature eliminates infrastructure management needs.

Challenges in Agent Deployment

Gartner Prediction

● Over 40% of agentic AI projects are predicted to be canceled by 2027.

● Reasons: growing costs, unclear business value, insufficient security.

● Key Challenges

● Specialized Runtime: Need for a runtime environment unlike typical applications.

● Stateless LLMs: Agents require memory to retain context and provide personalized experiences.

● Fine-Trained Identity: Access control to ensure agents only access appropriate systems and data.

● Custom Tool Interaction: Ensuring AI agents can discover and interact with custom tools and data sources with proper identity controls.

Autonomous Multi-Step Workflows

● Need for agents to execute complex, multi-step workflows independently.

● Requirement for common tools in workflows that are either shared or restricted.

● Specialized Monitoring

● Necessity for monitoring systems specifically designed for agentic AI.

● Differences between prototyping and production deployment of agents.

Amazon Bedrock AgentCore

Modular and Fast Iteration Approach

● Designed for building scalable production agents.

● Agent Core Runtime

● Secure, serverless runtime for deploying and scaling dynamic agents and tools.

● Supports various frameworks, protocols, and model choices.

● Reliable execution of multimodal, real-time, and long-running agents (up to 8 hours).

● Features checkpointing and recovery capabilities for graceful handling of interruptions or failures.

● Agent Core Gateway

● Facilitates integration of MCP servers and APIs with agents.

● Empowers agents with a variety of tools.

● Agent Core Browser and Code Interpreter

● Allows agents to act autonomously in the browser or execute code under specified terms and rules.

Supporting Tools

Agent Core Identity

● Builds enterprise-ready AI agents with security and identity features.

● Includes standards-based authentication, compatibility with existing identity providers, and OAuth support.

● Features a secure token vault for frictionless user experiences.

● Agent Core Memory

● Enables agents to store and retrieve short and long-term memory for complex workflows.

● Facilitates continuous use of user input and history in shared memory.

● Observability

● Centralizes observability stack for Gen AI, combining logs, traces, and metrics.

● Measures performance and accuracy at scale.

Coinbase's Use of Generative AI

Crypto and Economic Freedom

● Crypto empowers economic freedom and fair participation in the global economy.

● Coinbase’s mission is to expand economic freedom to over a billion people.

● Key Pillars at Coinbase

● Building Trust

● Safeguarding users' assets with top-tier security.

● Implementing strong protections against fraud and account takeovers.

● Making Crypto Accessible

● Designing intuitive and personalized experiences using AI and machine learning.

● Simplifying complicated financial products.

● Scaling Globally

● Operating efficiently and quickly to support millions of users across 100+ countries.

● Managing billions of dollars in trading volume.

User Journey and ML Applications

Login Security

● ML models defend against account takeovers during user login.

● Fiat to Crypto Conversion

● ML models evaluate the legitimacy of users and the risk of credit defaults when moving currency into the crypto exchange.

● Funds Transfer to Blockchain

● ML models assess risks of reversals before releasing funds onto the blockchain.

● Personalization of User Experience

● ML-Powered Personalization

● Personalized search results, tailored news feeds, intuitive recommendations, and real-time price alerts.

● Use of advanced deep learning models trained on massive datasets.

● Adaptive Risk Scoring System

● Built using graph neural networks to score blockchain addresses.

● Detects and blocks malicious blockchain addresses, protecting customers.

● ERC20 Scam Token Detection

● Combines smart contract auditing and machine learning.

● Reviews assets before listing on the exchange, providing additional customer protection.

Predictive Machine Learning Model

Volatility of Crypto Market

● Crypto markets are highly volatile, requiring a reliable infrastructure.

● Predictive Models

● Used to scale backend databases before surges occur, ensuring reliable performance during market volatility.

New Generation of AI Investments

Large Language Models (LLMs)

● Transformative impact since November 2022, marking a historic moment in AI.

● Key Areas of Application

● Customer Support: AI-powered virtual assistants and advanced tools for human agents.

● Compliance: Generative AI (GenAI) streamlines complex investigations and navigates regulatory landscapes.

● Developer Productivity: Improving developer productivity at Coinbase.

● Challenges in Crypto Customer Support

● Volatility: User activity can fluctuate by up to 50% within a month, requiring scalable support systems.

● Trust: Customers need to feel safe and supported regardless of market conditions.

● Global Operations: Supporting users across different languages, regulations, and expectations.

GenAI Strategy

● Internal GenAI platform integrating multiple LLMs and data sources via standard interfaces (OpenAI API standards

● and Model Context Protocol).

● Depth

● Targeted investments in high-impact areas within customer support:

● Automating customer-facing workflows with conversational chatbots.

● Providing human support agents with intelligent tools to improve effectiveness.

● Extracting valuable insights from support tickets to continually improve products and services.

● Use Cases

● Customers requires help, initiating a cycle of support interactions enhanced by GenAI.

Customer Support Chatbot

● Over 50% of customers prefer chat as their support channel.

● Trained on Coinbase's extensive knowledge base.

● Provides instant, accurate assistance around the clock, adapting to market volatility.

Development of the Chatbot

FAQ Style RAG Responses

● Simple, FAQ-style responses to automate common queries (e.g., sign-in issues, two-factor authentication).

● Focuses on accuracy to build a strong foundation.

Agentic Architecture

User Input and Conversation History

● Stored in a vector database serving as the memory layer.

● RAG Retriever

● Uses Bedrock knowledge bases to vectorize and store Coinbase help articles.

● Employs Cohere’s rerank models for high accuracy in retrieved articles.

● Response Generation

● Mixture of LLMs, including cloud models served through Bedrock.

● Involves a sub-agent developed in an actor-critic architecture for precise response generation.

● Guardrails

● Input and output guardrails powered by Bedrock guardrails.

● Protects against harmful content and sensitive PII leakage.

● Custom domain-specific filters to minimize prompt injection.

● Custom rules to keep responses grounded and reduce hallucinations.

Next Layer of Development

● Industry and LLM Capability Evolution

● Recognition of the need to achieve more than the initial RAG layer.

Cohere's Rerank model enhances search relevance

● Semantically reorders initial search results

● Understands query-document context beyond keywords

● Supports over 100 languages

● Handles complex data like JSON and code

● Improves Retrieval-Augmented Generation (RAG) systems

● Increases enterprise search accuracy

● Provides faster user experiences

● Functions as a powerful second stage in search pipelines

● Sorts results from keyword or vector search by deep relevance scores Key Features & Models

● Rerank-v3.5: state-of-the-art performance, enhanced reasoning, and broad data compatibility (text, JSON, code).

● Rerank-english-v3.0: English documents and semi-structured data.

● Rerank-multilingual-v3.0: For non-English content, supporting over 100 languages.

● Rerank 3 Nimble: Prioritize efficiency and lower latency for high-volume use cases like e-commerce search, improving conversion rates.

● How They Work

● Input: A user query and an initial list of documents (from a keyword search like Elasticsearch or a vector search).

● Processing: The Rerank model, using a cross-encoder architecture, jointly processes the query and each document to understand their semantic relationship.

● Output: An ordered list of documents, each assigned a relevance score, with the most contextually relevant results at the top.

● Use Cases & Benefits

● Enhanced Enterprise Search: Surfaces highly relevant information from complex internal data (invoices, code).

● Improved RAG Systems: Ensures factual grounding by providing the most pertinent snippets to LLMs.

● Multilingual Applications: Semantic search across diverse languages.

● Cost & Complexity Reduction: Integrates easily with existing search stacks (e.g., Elasticsearch) via a single API call, adding semantic power without a full overhaul.

Cross-encoder architecture

● A model that processes two inputs (like a query and a document) jointly in a single sequence, using cross-attention mechanisms between all tokens to determine their relevance.

● Key Characteristics

● Joint Processing: The core feature is that the query and the candidate document are concatenated into a single input sequence, separated by a special token.

● Cross-Attention: Within the transformer layers, the model's self-attention mechanism can directly compare every token in the query with every token in the document. This fine-grained interaction allows for a deep, nuanced understanding of their relationship and semantic relevance.

● Single Output: The model typically outputs a single relevance score (e.g., a number between 0.0 and 1.0) for the pair, often using the embedding from the token passed through a classification layer.

● Role in Modern AI Systems:

● They are primarily deployed as re-rankers in a two-stage retrieval process, particularly in Retrieval-Augmented Generation (RAG) systems:

● First Stage (Bi-encoder): A fast, efficient bi-encoder model quickly narrows down a vast corpus of documents to a smaller, manageable list of potentially relevant candidates (e.g., the top 100).

● Second Stage (Cross-encoder): The cross-encoder then meticulously re-evaluates and re-ranks this smaller subset of candidates with high precision, ensuring the most accurate and contextually appropriate results are presented to the final LLM for generating a response.

Business Procedure Layer

● Enhanced Chatbot Capabilities

● As LLM capabilities improved, the chatbot was enhanced to follow business procedures autonomously.

● Collects information conversationally and takes direct actions on behalf of the user.

● Business procedures serve as a single source of truth, used by both humans and AI agents.

● Automates queries about user accounts and pending transactions.

Agentic Architecture

● Introduction of the Business

● Procedure Classifier.

● Routes queries to the correct sub-agent emulating specific business procedures.

● RAG agent as a specialized sub-agent executing procedures involving the RAG knowledge base.

Proactive Issue Resolution Layer (Anticipatory Capabilities)

● AI agents anticipate common user problems using user signals and active incidents on the platform.

● Proactive issue resolution layer added to the agentic design.

● Proactive agents fall back to the business procedure classifier if no proactive resolution is found.

● Data access for proactive agents powered through standardized MCP servers.

Key Design Principles

Model Selection

● Balanced approach considering accuracy, latency, and scalability.

● Continuous reevaluation as model capabilities evolve.

● Tool Standardization

● Standardization of tool access through

● Model Context Protocol (MCP).

● Strong foundation for customer support and other domains.

● Business Adaptability

● Single source of truth for business procedures powering both humans and AI.

● Enhanced business adaptability.

● Grounded Responses

● Emphasis on factual correctness in responses.

Monitoring and Evaluation

● LLM as a Judge Evaluations

● Assessment of chatbot responses for relevancy, accuracy, potential bias, hallucinations, and more.

● Active tracking of quality metrics and trends to quickly identify anomalies.

Agent Assist Tool

Purpose

● Designed to help human customer support agents handle complex issues.

● Provides real-time assistance for diagnosing and mitigating customer issues.

● Features

● Draws from account signals, ongoing incident data, and past customer support tickets.

● Offers personalized guidance and generates precise responses in multiple languages.

● Impact

● Faster resolutions, happier customers, and improved efficiency for support teams.

● Close to 65% of customer contacts are handled automatically by AI systems.

● Annual savings of nearly 5 million employee hours and significant cost savings.

● 65% of automated cases are resolved in a single interaction, enhancing user experience.

Importance of Compliance

● Critical for a fintech entity like Coinbase to uphold standards in AML/CFT (Anti-Money

● Laundering/Countering Terrorist Financing).

● Commitment to preventing malpractices on the platform.

● Compliance Processes

● Implementation of KYC, KYB, and TMS.

● Heavy reliance on human resources and thorough investigations.

● Need for detailed explainability and adaptability to diverse regulatory requirements across countries.

● Strategy for Compliance

● Leverage of the internal platform for accessing LLMs and data through MCP interfaces.

● Inclusion of a traditional machine learning model to detect high-risk compliance cases, built on the AnyScale-based ML platform.

● Depth

● Advanced deep learning models for detecting high-risk cases

● in KYC, KYB, and TMS.

● Automation and acceleration of complex investigations through AI.

● Gathering and synthesizing data from various sources for rigorous investigations across compliance workflows.

Compliance Assist Tool

Purpose

● Assistive tool for compliance agents, analogous to the customer support agent assist tool.

● Features

● AI-powered gathering and synthesis of information from internal systems and open-source intelligence.

● Composition of a narrative summary and clear risk review signals.

● Provides compliance agents with a fully documented, explainable investigation for well-informed, confident decision-making.

● Impact

● Empowerment of compliance teams and elevation of compliance operation standards.

Ray-powered Scalability

● Optimizes and manages Ray clusters.

● Efficient distribution and scaling across thousands

● of CPUs and GPUs.

● Handles massive datasets and complex models.

● Unified Developer Experience:

● Streamlined workflow from experimentation to production.

● Interactive development consoles.

● Built-in IDEs.

● Tools for debugging distributed workloads.

● Aims to accelerate developer productivity.

● Production Readiness:

● Adds enterprise-grade features on top of Ray.

● Advanced observability.

● Data governance.

● Cost tracking.

● Robust administration tools.

● Ensures reliability, security, and performance of ML models.

● Cloud Agnostic Deployment:

● Supports deployment across various cloud providers

● (AWS, Google Cloud).

● Supports on-premise Kubernetes clusters.

● Allows organizations to leverage existing infrastructure.

● Maintains control over data and resources.

● Optimizations and Integrations:

● Includes optimizations for performance and cost reduction.

● Integrations with essential ML tools:

● Weights & Biases for experiment tracking.

● Astronomer for workflow orchestration (using

● Apache Airflow).

● Focus on AI Democratization:

● Simplifies complexities of distributed computing.

● Aims to make advanced AI and ML capabilities more accessible.

● Ray AI Libraries

● Ray Data: For scalable data processing, particularly suited for unstructured data like images, audio, and video.

● Ray Train: For distributed model training, with integrations for frameworks like

● PyTorch, PyTorch Lightning, and Hugging Face Transformers.

● Ray Tune: A framework for scalable and fault-tolerant hyperparameter tuning and experiment execution.

● Ray Serve: For deploying and serving machine learning models as scalable, programmable APIs with low latency.

● RLlib: An industry-grade library for reinforcement learning, supporting a wide range of applications from robotics to finance.

Agentic Architecture for Compliance

Initiation

● The process begins when deep learning compliance risk models trigger alerts on high-risk cases.

● Initiates a holistic review, a comprehensive investigation of high-risk compliance cases.

● Compliance Auto Resolution Engine (CAR)

● Central to the solution, acting as the orchestrator for the holistic review process.

● Mobilizes both automation and human expertise in an agentic AI-driven workflow.

● Human-in-the-Loop Process

● Interaction with two human personas:

● Compliance Operations Agents: Review AI system findings and provide feedback for improvement.

● End Customers: Reach out for additional information when necessary.

● Data Aggregation and Synthesis

● Engine aggregates and synthesizes data from various sources (internal, external, open-source intelligence) via standardized MCP data connectors.

● Results in a robust AI-generated report called the narrative summary.

● Final Decision

● Human compliance operations agent reviews AI's reasoning and compliance evidence.

● Determines whether a case warrants filing a suspicious activity report with government authorities.

Developer Productivity

AI in SDLC

● AI agents assist engineering teams at various stages of the software development life cycle (SDLC).

● Consolidates tasks like coding, ticketing, documenting, pull request creation, and reviewing pull requests.

● Focus Areas

● Code-Authoring

● Code Reviewing

● Quality Assurance

Code-Authoring Tools

Cloud Code

● State-of-the-art coding assistant integrating directly into IDEs or command line.

● Cursor

● Intelligent, context-aware IDE designed for faster, more collaborative, and efficient software development.

● Offered as an option for developers to choose based on their preferences.

● Powering Models

● Tools powered by Anthropic models served through Bedrock.

Pull Request and Code Reviewing

Homegrown Tool

● Adapted from an open-source tool and enhanced with models from Bedrock.

● Implemented as an AI-powered GitHub action to automate PR reviews.

● Definition:

● A "Homegrown Tool" is software, applications, or solutions developed internally by a company or community to address specific, unique problems.

● Development Approach:

● Often created using custom scripts.

● Utilizes existing platforms like Visio or Excel.

● Avoids reliance on off-the-shelf products.

● Features

● Summarizes pull requests and underlying code changes.

● Generates clear natural language review comments.

● Enforces coding conventions.

● Highlights gaps in unit testing coverage.

● Provides tips for debugging CI failures.

● Impact

● Automates routine aspects of code review, allowing human developers to focus on nuanced aspects and deliver higher value.

Quality Assurance

Automated UI Testing Tool

● Homegrown AI-powered tool for end-to-end quality assurance automation for web and mobile UI.

● Converts natural language test descriptions into autonomous browser actions.

● Features

● Performs browser actions in various form factors using services like BrowserStack.

● Emulates browser actions through frameworks like Playwright.

● Benefits

● Allows testing UI like a human would, enhancing the effectiveness of quality assurance processes.

Impact of AI on Developer Productivity

Code Generation

● Nearly 40% of all code written daily at Coinbase is AI-generated or AI-influenced.

● Goal to surpass 50% soon, with human review and understanding still required.

● Automated PR Reviews

● Estimated annual savings of about 75,000 hours.

● Raises code quality by enforcing coding conventions.

● Quality Assurance

● AI-powered UI testing tool achieves accuracy on par with human testers.

● Detects three times as many bugs as a human in the same amount of time.

● Dramatically speeds up the process of introducing new tests (as little as 15 minutes).

● 86% cost reduction compared to traditional manual testing.