Tech • IA • Crypto
Anthropic Routines, Claude 4.7, AWS Bedrock Deals
Briefing
Anthropic unveils Claude Code Routines
Anthropic introduced Routines for Claude Code, enabling agents to run autonomously on schedules or event triggers. Developers define prompts, tools, repositories, and triggers while Anthropic handles infrastructure and execution. The system supports GitHub events, cron-like scheduling, and custom webhooks for automation. This marks a shift from reactive prompts to persistent, proactive software agents.
AWS deepens Anthropic partnership
Amazon Web Services expanded its strategic alignment with Anthropic, backed by multi-billion-dollar investments and long-term infrastructure commitments exceeding $100 billion. Claude models are delivered عبر Amazon Bedrock, a dedicated Claude platform, and developer tooling integrations. The partnership emphasizes enterprise-grade privacy with zero operator access guarantees. AWS positions itself as the primary cloud backbone for Claude deployment.
Claude 4.7 powers Microsoft Foundry
Microsoft Foundry integrates Claude Sonnet 4.6 and Claude Opus 4.7 to support enterprise-grade agent systems. The platform connects with Visual Studio Code, GitHub, and over 1,400 connectors including SAP and ServiceNow. Microsoft emphasizes production readiness, observability, and governance for multi-step AI workflows. The move reinforces Claude’s role in enterprise reasoning and long-context tasks.
Anthropic pushes autonomous coding workflows
New Claude Code updates enable more independent execution with safety-aware autonomy checks. The system can proceed without approval in low-risk scenarios, reducing interruptions in development workflows. Features like remote session control and cross-device continuity support asynchronous coding. A redesigned terminal improves performance with virtualized rendering and stable memory usage.
Cloud-managed agents redefine infrastructure
Anthropic highlighted a shift from model limitations to infrastructure bottlenecks in agent deployment. New cloud-managed systems bundle memory, orchestration, identity, and tool access into unified primitives. Agents operate as long-running systems with persistent sessions and secure credentials. This enables outcome-based workflows where users assign goals instead of step-by-step prompts.
Memory and ‘dreaming’ boost agents
Anthropic introduced persistent memory systems and experimental “dreaming” mechanisms for continuous learning. Agents store knowledge in file-based structures, improving performance across repeated tasks. Shared memory enables collaboration between multiple agents within the same environment. The approach allows compounding intelligence rather than isolated task execution.
Test-time compute tradeoffs emerge
Increasing test-time compute improves model reasoning by allocating more tokens during inference. Higher effort leads to better accuracy in coding, simulation, and reasoning tasks, but raises latency and cost. Gains show diminishing returns at extreme levels, complicating optimization decisions. Teams must balance speed, cost, and quality in production deployments.
Enterprises scale agents across industries
Companies like Omni, Man Group, and Legora demonstrate real-world agent adoption across analytics, finance, and legal sectors. Use cases include natural language querying, systematic trading signals, and end-to-end legal workflows. Success depends heavily on structured data, evaluation systems, and governance. These deployments signal a transition from experimentation to production-grade AI systems.
Videos covered
Build a proactive agent workflow with Claude Code
- •Shift from reactive tools to proactive agents
- •Infrastructure challenges addressed
- •Introduction of “routines” feature
Build AI agents using Claude in Microsoft Foundry
- •Shift to Agentic AI Systems
- •Microsoft Foundry as a Unified Platform
- •Claude Models for Advanced Reasoning
AI with Claude on AWS: From code to orchestration
- •Deepening AWS–Anthropic partnership
- •Project Rainier and custom AI chips
- •Three deployment paths for Claude on AWS
What's new in Claude Code
- •Shift Toward Autonomy
- •Remote Control Across Devices
- •Flicker-Free Terminal Interface
From one person to 80: Scaling a hypergrowth engineering org with Claude Code
- •Rapid early growth and profitability
- •Scaling from 1 to 15 engineers
- •AI-driven onboarding replaces documentation
The thinking lever
- •Test-Time Compute Boosts Performance
- •Token Usage Directly Impacts Output Quality
- •Diminishing Returns at Maximum Effort
How to get to production faster with Claude Managed Agents
- •Shift from model limits to infrastructure bottlenecks
- •Rise of outcome-oriented agents
- •Need for secure access and identity
Build a production-ready agent with Claude Managed Agents
- •API-first agent infrastructure
- •Core building blocks: agents, environments, sessions, events
- •Fine-grained tool access and permissions
What legal agents inherit from coding agents: Lessons from Legora
- •Rapid growth of Legora
- •Coding agents as a blueprint
- •Shared structure of knowledge work
Building the best agentic analytics harness: Powered by Claude, built with Claude Code
- •Rapid engineering acceleration
- •Natural language to data queries
- •Semantic layer as core infrastructure
Trading signals that trade themselves
- •AI enters high-stakes asset management
- •Systematic trading as a prime AI use case
- •Backtesting remains central to validation
Picking the right model
- •Model selection is more complex than it թվում
- •Three core decision pillars: quality, latency, cost
- •Public benchmarks offer limited real-world value
Memory and dreaming for self learning agents
- •Agents Are Expanding in Capability and Duration
- •Memory Enables Continuous Learning
- •File-Based Design Aligns With Model Strengths