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Research Report: Reinventing AWS – Insights from AWS re:Invent 2025

Executive Summary

• AWS re:Invent 2025 centers on “agentic AI” as the overarching theme, signaling a shift toward autonomous AI-enabled cloud workloads and software agents that can operate with less human intervention. The event program features five AWS leadership keynotes, more than 600 to 1,000+ technical sessions across a spectrum of formats, and a broad ecosystem of hands-on labs, certification activities, and partner demonstrations. The emphasis is on foundational AI infrastructure, agent-based capabilities, and scalable production deployments across industries. Key product and capability themes include AWS Bedrock AgentCore, frontier agents, Nova Forge services, Trainium-based acceleration, and new infrastructure innovations (e.g., Graviton5, Trainium updates) [AWS re:Invent 2025: What to know][AWS re:Invent 2025 Agenda | Amazon Web Services].
• The conference also foregrounds practical, real-world use cases, including customer expe

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History of Datadog and Its Founders: A Comprehensive Research Report

Executive summary

• Datadog was founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, two software engineers with backgrounds in infrastructure and development. The founders initially bootstrapped the company, launching a SaaS platform aimed at providing real-time, unified visibility into complex IT environments. This focus positioned Datadog to address the growing needs of cloud-native operations and observability. What is Brief History of Datadog Company?
• Early growth centered on expanding beyond basic infrastructure monitoring to include application performance monitoring (APM) and log management, culminating in a broader “unified observability” approach. The company attracted Series A, B, and C funding in the 2012–2015 window, helping scale product development and sales. [What is Brief History of Datadog Company?](https://canvasbusinessmodel.com/blogs/brief-

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Temporal for AI Agents and Viable Alternatives: A Comprehensive Research Report

Executive summary

• Temporal offers a durable, deterministic orchestration layer that enables long-running AI agents to operate reliably in production. The key insight is to separate deterministic workflow execution from non-deterministic LLM decisions: the workflow path is replay-safe and durable, while the decisions driven by LLMs (which tools to call, what plan to adopt) can be non-deterministic. This separation enables agents to survive crashes and resume exactly where they left off, reusing the event history rather than recomputing past decisions. Temporal is widely presented as well-suited for AI agents, with examples including the OpenAI Codex agent built on Temporal. Of course you can build dynamic AI agents with Temporal
• Practical implementations demonstrate how to wire AI agents with Temporal by treating LLM-driven decisions as non-

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Durable AI Agents: Temporal Horizons, Architectures, and Alternatives

Executive summary

• There is growing interest in how durable AI agents can perform long-horizon tasks autonomously. A leading approach to understanding this durability is to measure the task horizon that AI agents can complete with a given reliability, rather than focusing solely on single-step benchmarks.
• The METR project documents a robust, exponential improvement in the length of tasks generalist autonomous agents can complete. They report a doubling time of about 7 months over the last 6 years, with current public frontier models able to complete tasks that take expert humans hours to finish, and a forecast that month-long tasks could be feasible for frontier agents by the end of the decade under current trends. However, there is substantial uncertainty, and real-world impact depends on safety, robustness, and alignment alongside capability growth [Measuring AI Ability to Complete Long Tasks - METR](https://metr.org/blog/2025-03-19

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Durable AI Agents, Temporal, and Alternatives — Research Report

Date: 2025-11-20
Author: Research Analyst

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Executive summary

Durable AI agents are autonomous software entities designed to run long-lived, stateful, and recoverable workflows that reason, act, and interact over extended periods. Durable agents require persistent state, identity, reliable orchestration, idempotent tool calls, and observability to handle retries, failures, human-in-the-loop steps, and multimodal inputs.

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Durable AI Agents with Temporal and Alternatives

A Research Report

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1. Executive Summary

Durable AI agents are AI-driven systems (often LLM-based) that can run reliably over long periods, survive failures, coordinate complex tool calls, and maintain state across interactions. As developers move from one-off “chat completions” to agentic systems that plan, act, and interact with external services, durability and orchestration become central engineering concerns—not just AI concerns.

Temporal has emerged as a prominent orchestration platform for building durable agents. It addresses classic distributed-systems problems—failures, retries, long-running tasks, state management, and observability—and applies them to agentic AI: single agents, multi-agent systems, ambient/proactive agents, and retrieval-augmented generation (RAG) workflows. Tutorials and case studies show Temporal paired with Python, FastAPI, Chainlit, MCP (Model Context Protocol), OpenAI’s Agents SDK, and others to create production

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Durable AI Agents, Temporal Reasoning, and Emerging Alternatives

A Research Synthesis

Executive Summary

Durable AI agents—systems that persist over time, retain state, and act autonomously across extended horizons—are rapidly moving from concept to practice. They differ from “stateless” LLM tools by adding:

• Long-term, mutable memory
• Recursive planning and reflection
• Tool and actuator control in closed feedback loops

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Durable AI Agents with Temporal and Alternatives

A Research Report

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1. Executive Summary

Durable AI agents are AI systems—often powered by large language models (LLMs)—that can reliably execute long‑running, stateful workflows in the presence of failures, restarts, and external dependencies. As organizations move from prototypes to production, reliability, observability, and control become central concerns. This has led to a growing interest in combining agentic AI with durable execution platforms such as Temporal.

This report:

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Research Report on Durable AI Agents, Temporal Solutions, and Alternatives

Executive Summary

The emergence of durable AI agents marks a significant advancement in the development of artificial intelligence systems capable of handling complex workflows in real-world settings. These agents are designed to maintain their state and progress through failures, thereby ensuring reliability in long-running tasks. This report explores various frameworks and technologies that facilitate the creation of durable AI agents, focusing on Pydantic AI, Microsoft Azure's Durable Task framework, Restate, Temporal, and DBOS. By synthesizing information from multiple sources, this report aims to provide a comprehensive understanding of durable AI agents, their functionalities, and the alternatives available in the market.

Introduction

Durable AI agents are sophisticated systems that leverage artificial intelligence and machine learning models to perform tasks while ensuring resilience against failures. These agents ca

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Research Report: British Restaurants in New York City

Executive Summary

New York City has experienced a notable increase in the presence of British restaurants, reflecting a growing appreciation for British cuisine among local diners. This report examines various establishments offering British fare, from traditional pubs to modern bistros, highlighting their unique offerings and atmospheres. Key players in this culinary landscape include Jones Wood Foundry, Lord's, Hawksmoor, and Gordon Ramsay's Fish and Chips. By analyzing these establishments, we can gain insights into the evolving perceptions of British cuisine, the cultural significance of these restaurants, and their contribution to NYC's diverse dining scene.

Introduction

British cuisine has historically suffered from stereotypes of being bland and unexciting. However, recent trends show a resurgence of interest, with many British restaurants opening in New York City. These establishments offer a mix of traditional dishes and modern interpr