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CrowdStrike, Cisco and Palo Alto Networks all shipped agentic SOC tools at RSAC 2026 — the agent behavioral baseline gap survived all three

CrowdStrike CEO George Kurtz highlighted in his RSA Conference 2026 keynote that the fastest recorded adversary breakout time has dropped to 27 seconds. The average is now 29 minutes, down from 48 minutes in 2024. That is how much time defenders have before a threat spreads. Now CrowdStrike sensors detect more than 1,800 distinct AI applications running on enterprise endpoints, representing nearly 160 million unique application instances. Every one generates detection events, identity events, and data access logs flowing into SIEM systems architected for human-speed workflows. Cisco found that 85% of surveyed enterprise customers have AI agent pilots underway. Only 5% moved agents into production, according to Cisco President and Chief Product Officer Jeetu Patel in his RSAC blog post. That 80-point gap exists because security teams cannot answer the basic questions agents force. Which agents are running, what are they authorized to do, and who is accountable when one goes wrong. “The number one threat is security complexity. But we’re running towards that direction in AI as well,” Etay Maor, VP of Threat Intelligence at Cato Networks, told VentureBeat at RSAC 2026. Maor has attended the conference for 16 consecutive years. “We’re going with multiple point solutions for AI. And now you’re creating the next wave of security complexity.” Agents look identical to humans in your logs In most default logging configurations, agent-initiated activity looks identical to human-initiated activity in security logs. “It looks indistinguishable if an agent runs Louis’s web browser versus if Louis runs his browser,” Elia Zaitsev, CTO of CrowdStrike, told VentureBeat in an exclusive interview at RSAC 2026. Distinguishing the two requires walking the process tree. “I can actually walk up that process tree and say, this Chrome process was launched by Louis from the desktop. This Chrome process was launched from Louis’s Claude Cowork or ChatGPT application. Thus, it’s agentically con

Slack adds 30 AI features to Slackbot, its most ambitious update since the Salesforce acquisition

Slack today announced more than 30 new capabilities for Slackbot, its AI-powered personal agent, in what amounts to the most sweeping overhaul of the workplace messaging platform since Salesforce acquired it for $27.7 billion in 2021. The update transforms Slackbot from a simple conversational assistant into a full-spectrum enterprise agent that can take meeting notes across any video provider, operate outside the Slack application on users' desktops, execute tasks through third-party tools via the Model Context Protocol (MCP), and even serve as a lightweight CRM for small businesses — all without requiring users to install anything new. The announcement, timed to a keynote event that Salesforce CEO Marc Benioff is headlining Tuesday morning, arrives less than three months after Slackbot first became generally available on January 13 to Business+ and Enterprise+ subscribers. In that short window, Slack says the feature is on track to become the fastest-adopted product in Salesforce's 27-year history, with some employees at customer organizations reporting they save up to 90 minutes per day. Inside Salesforce itself, teams claim savings of up to 20 hours per week, translating to more than $6.4 million in estimated productivity value. "Slackbot is smart. It's pleasant, and I think it's endlessly useful," Rob Seaman, Slack's executive vice president and general manager, told VentureBeat in an exclusive interview ahead of the announcement. "The upper bound of use cases is effectively limitless for it." The release signals Slack's clearest bid yet to become what Seaman and the company's leadership describe as an "agentic operating system" — a single surface through which workers interact with AI agents, enterprise applications, and one another. It also marks a direct challenge to Microsoft, which has spent the past two years embedding its Copilot assistant across the entirety of its productivity stack. From simple chatbot to autonomous coworker: six new capabilities that

Claude Code's source code appears to have leaked: here's what we know

Anthropic appears to have accidentally revealed the inner workings of one of its most popular and lucrative AI products, the agentic AI harness Claude Code, to the public. A 59.8 MB JavaScript source map file (.map), intended for internal debugging, was inadvertently included in version 2.1.88 of the @anthropic-ai/claude-code package on the public npm registry pushed live earlier this morning. By 4:23 am ET, Chaofan Shou (@Fried_rice), an intern at Solayer Labs, broadcasted the discovery on X (formerly Twitter). The post, which included a direct download link to a hosted archive, acted as a digital flare. Within hours, the ~512,000-line TypeScript codebase was mirrored across GitHub and analyzed by thousands of developers. For Anthropic, a company currently riding a meteoric rise with a reported $19 billion annualized revenue run-rate as of March 2026, the leak is more than a security lapse; it is a strategic hemorrhage of intellectual property.The timing is particularly critical given the commercial velocity of the product. Market data indicates that Claude Code alone has achieved an annualized recurring revenue (ARR) of $2.5 billion, a figure that has more than doubled since the beginning of the year. With enterprise adoption accounting for 80% of its revenue, the leak provides competitors—from established giants to nimble rivals like Cursor—a literal blueprint for how to build a high-agency, reliable, and commercially viable AI agent. Anthropic confirmed the leak in a spokesperson’s e-mailed statement to VentureBeat, which reads: “Earlier today, a Claude Code release included some internal source code. No sensitive customer data or credentials were involved or exposed. This was a release packaging issue caused by human error, not a security breach. We're rolling out measures to prevent this from happening again.” The anatomy of agentic memory The most significant takeaway for competitors lies in how Anthropic solved "context entropy"—the tendency for AI agents to

RSAC 2026 shipped five agent identity frameworks and left three critical gaps open

“You can deceive, manipulate, and lie. That’s an inherent property of language. It’s a feature, not a flaw,” CrowdStrike CTO Elia Zaitsev told VentureBeat in an exclusive interview at RSA Conference 2026. If deception is baked into language itself, every vendor trying to secure AI agents by analyzing their intent is chasing a problem that cannot be conclusively solved. Zaitsev is betting on context instead. CrowdStrike’s Falcon sensor walks the process tree on an endpoint and tracks what agents did, not what agents appeared to intend. “Observing actual kinetic actions is a structured, solvable problem,” Zaitsev told VentureBeat. “Intent is not.” That argument landed 24 hours after CrowdStrike CEO George Kurtz disclosed two production incidents at Fortune 50 companies. In the first, a CEO's AI agent rewrote the company's own security policy — not because it was compromised, but because it wanted to fix a problem, lacked the permissions to do so, and removed the restriction itself. Every identity check passed; the company caught the modification by accident. The second incident involved a 100-agent Slack swarm that delegated a code fix between agents with no human approval. Agent 12 made the commit. The team discovered it after the fact. Two incidents at two Fortune 50 companies. Caught by accident both times. Every identity framework that shipped at RSAC this week missed them. The vendors verified who the agent was. None of them tracked what the agent did. The urgency behind every framework launch reflects a broader market shift. "The difficulty of securing agentic AI is likely to push customers toward trusted platform vendors that can offer broader coverage across the expanding attack surface," according to William Blair's RSA Conference 2026 equity research report by analyst Jonathan Ho. Five vendors answered that call at RSAC this week. None of them answered it completely. Attackers are already inside enterprise pilots The scale of the exposure is already visible

Glia wins Excellence Award for safer AI in banking

Glia, a customer service platform providing AI-powered interactions for the banking sector, has been named a winner in the Banking and Financial Services Category at the 2026 Artificial Intelligence Excellence Awards. The awards recognises achievements in a range of industries and use cases, spotlighting “companies and leaders moving AI beyond experimentation and into practical, accountable […] The post Glia wins Excellence Award for safer AI in banking appeared first on AI News.

Founders building AI-powered SaaS — what multiplier are you using from token cost to customer price? 3x? 5x? 10x? Curious to see how others are pricing inference.

Founders building AI-powered SaaS — what multiplier are you using from token cost to customer price? 3x? 5x? 10x? Curious to see how others are pricing inference.

Bump ruby_llm from 1.2.0 to 1.13.2

Bumps [ruby_llm](https://github.com/crmne/ruby_llm) from 1.2.0 to 1.13.2. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/crmne/ruby_llm/releases">ruby_llm's releases</a>.</em></p> <blockquote> <h2>1.13.2</h2> <h1>RubyLLM 1.13.2: Patch Fixes for Schema + Streaming 🐛🔧</h1> <p>A small patch release with three fixes.</p> <h2>🧩 Fix: Schema Names Are Always OpenAI-Compatible</h2> <p>Schema names now always produce a valid <code>response_format.json_schema.name</code> for OpenAI:</p> <ul> <li>namespaced names like <code>MyApp::Schema</code> are sanitized</li> <li>blank names now safely fall back to <code>response</code></li> </ul> <p>Fixes <a href="https://redirect.github.com/crmne/ruby_llm/issues/654">#654</a>.</p> <h2>🌊 Fix: Streaming Ignores Non-Hash SSE Payloads</h2> <p>Streaming handlers now skip non-Hash JSON payloads (like <code>true</code>) before calling provider chunk builders, preventing intermittent crashes in Anthropic streaming.</p> <p>Fixes <a href="https://redirect.github.com/crmne/ruby_llm/issues/656">#656</a>.</p> <h2>🗓️ Fix: models.dev <code>created_at</code> Date Handling</h2> <p>Improved handling for missing <code>models.dev</code> dates when populating <code>created_at</code> metadata.</p> <h2>Installation</h2> <pre lang="ruby"><code>gem &quot;ruby_llm&quot;, &quot;1.13.2&quot; </code></pre> <h2>Upgrading from 1.13.1</h2> <pre lang="bash"><code>bundle update ruby_llm </code></pre> <h2>Merged PRs</h2> <ul> <li>Fix missing models.dev date handling for created_at metadata by <a href="https://github.com/afurm"><code>@​afurm</code></a> in <a href="https://redirect.github.com/crmne/ruby_llm/pull/652">crmne/ruby_llm#652</a></li> <li>[BUG] Fix schema name sanitization for OpenAI API compatibility by <a href="https://github.com/alexey-hunter-io"><code>@​alexey-hunter-io</code></a> in <a href="https://redirect.github.com/crmne/ruby_llm/pull/655">crmne/ruby_llm#655</a></li> <li>Fix Anthropic streaming crash on non-hash SSE payloads by <a href="https://github.com/crmne"><code>@​crmne</code></a> in <a href="https://redirect.github.com/crmne/ruby_llm/pull/657">crmne/ruby_llm#657</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/crmne/ruby_llm/compare/1.13.1...1.13.2">https://github.com/crmne/ruby_llm/compare/1.13.1...1.13.2</a></p> <h2>1.13.1</h2> <h1>RubyLLM 1.13.1: Quick Fixes 🐛🔧</h1> <p>A small patch release with three fixes.</p> <h2>🧩 Fix: Schema + Tool Calls No Longer Crash</h2> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/crmne/ruby_llm/commit/0950693544457b840cee7f7c89e69248f5f32de6"><code>0950693</code></a> Updated cassettes</li> <li><a href="https://github.com/crmne/ruby_llm/commit/b6e62a6df45e6445a91d41ff71440deec1ea8d88"><code>b6e62a6</code></a> Bump to 1.13.2</li> <li><a href="https://github.com/crmne/ruby_llm/commit/67c41488c3fba7af4eea5777c799c3e4789bb38e"><code>67c4148</code></a> Fix Anthropic streaming crash on non-hash SSE payloads (<a href="https://redirect.github.com/crmne/ruby_llm/issues/657">#657</a>)</li> <li><a href="https://github.com/crmne/ruby_llm/commit/afe7d046eb102c42137ee1aac338df861bca8637"><code>afe7d04</code></a> [BUG] Fix schema name sanitization for OpenAI API compatibility (<a href="https://redirect.github.com/crmne/ruby_llm/issues/655">#655</a>)</li> <li><a href="https://github.com/crmne/ruby_llm/commit/fe8994d56640d56112d1afa82d95014a2df967d6"><code>fe8994d</code></a> Updated models</li> <li><a href="https://github.com/crmne/ruby_llm/commit/48bf6f6c98a15ed53501bdabc36cc9fc740bec30"><code>48bf6f6</code></a> Updated models</li> <li><a href="https://github.com/crmne/ruby_llm/commit/d940984d0b3cf63f108a7e0df52db82238ff8549"><code>d940984</code></a> Fix missing models.dev date handling for created_at metadata (<a href="https://redirect.github.com/crmne/ruby_llm/issues/652">#652</a>)</li> <li><a href="https://github.com/crmne/ruby_llm/commit/97c0546d8e09cfd51943ae612d9598ab11e81885"><code>97c0546</code></a> Bump to 1.13.1</li> <li><a href="https://github.com/crmne/ruby_llm/commit/10949459d1243cc2a7c1ebef3a8ce9ac9691bb71"><code>1094945</code></a> Populate Gemini cached token usage</li> <li><a href="https://github.com/crmne/ruby_llm/commit/beec83752c7697465b0f6f9e50b8bdfbc25353d1"><code>beec837</code></a> Fix schema JSON parsing for intermediate tool-call responses (<a href="https://redirect.github.com/crmne/ruby_llm/issues/650">#650</a>)</li> <li>Additional commits viewable in <a href="https://github.com/crmne/ruby_llm/compare/1.2.0...1.13.2">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compat

[AI Agent] Master Tracking: Complete AI Agent Implementation

## 🎯 Goal Track the complete implementation of autonomous Python AI Agent for CoffeeOrderSystem. --- ## 📋 Implementation Steps ### Phase 1: Infrastructure (Week 1) - [ ] **Step 1:** Setup Python AI Agent Service infrastructure #133 - Python service with FastAPI - Docker integration - Basic health checks - Makefile commands ### Phase 2: AI Integration (Week 1-2) - [ ] **Step 2:** Implement Cognee integration with semantic code search #134 - CogneeService with RAG search - Architecture context gathering - Entity file discovery - Integration tests - [ ] **Step 3:** Implement PlannerAgent with LangChain #135 - TaskPlan models - LangChain planning chain - Prompt templates - Plan generation and posting ### Phase 3: Code Generation (Week 2-3) - [ ] **Step 4:** Implement DevAgent for code generation #136 - GitHub file operations - Code generation prompts - Create/modify/delete operations - Branch management - [ ] **Step 5:** Implement PRAgent and workflow orchestration #137 - PR creation with rich descriptions - Workflow orchestrator - Background task processing - Complete end-to-end flow ### Phase 4: Advanced Features (Week 3-4) - [ ] **Step 6:** Add learning/memory system - Store successful patterns in Cognee - Learn from PR reviews - Avoid failed patterns - Improve over time - [ ] **Step 7:** Add GitHub webhook listener - Auto-trigger on issue label - Real-time processing - Queue management - Concurrent task handling --- ## 🎯 Success Criteria ### MVP (Minimum Viable Product) - ✅ Agent creates plans for issues - ✅ Agent generates compilable code - ✅ Agent creates PRs with descriptions - ✅ Works for simple tasks (add field, update config) - ✅ Error handling with GitHub notifications ### Production Ready - ☐ Handles complex multi-layer changes - ☐ Learns from successful PRs - ☐ Automatic triggering via webhooks - ☐ Rate limiting and queue management - ☐ Comprehensive test coverage (>80%) - ☐ Monitoring and metrics --- ## 📊 Architecture Overview ``` ┌────────────────────────────────────┐ │ GitHub Issues (labeled: ai-agent) │ └───────────────┬────────────────────┘ │ ↓ (webhook or manual trigger) ┌───────────────┼────────────────────┐ │ WorkflowOrchestrator │ └───────────────┬────────────────────┘ │ ┌────────┼────────┐ │ │ │ ↓ ↓ ↓ PlannerAgent DevAgent PRAgent │ │ │ │ │ │ ┌───┼───┐ │ ┌───┼───┐ │ │ │ │ │ │ │ ↓ ↓ ↓ ↓ ↓ ↓ ↓ Cognee LLM GitHub GitHub (RAG) (GPT-4) (API) ``` ### Component Responsibilities **PlannerAgent:** - Analyzes GitHub issue - Searches codebase via Cognee - Creates structured TaskPlan - Posts plan as issue comment **DevAgent:** - Generates code via LLM - Creates/modifies/deletes files - Commits to feature branch - Preserves code style **PRAgent:** - Creates pull request - Writes comprehensive PR description - Links to original issue - Adds testing checklist **WorkflowOrchestrator:** - Coordinates all agents - Handles errors - Posts progress updates - Manages background execution --- ## 📦 Tech Stack ### Core - **Python 3.11+** - Agent runtime - **FastAPI** - Web framework - **LangChain** - LLM orchestration - **OpenAI GPT-4** - Code generation - **PyGithub** - GitHub API client ### AI/ML - **Cognee** - RAG and semantic search - **OpenAI Embeddings** - Vector search - **LangChain Chains** - Prompt management ### Infrastructure - **Docker** - Containerization - **PostgreSQL** - Shared with .NET API - **uvicorn** - ASGI server --- ## 📝 Usage Example ### 1. Create Issue ```markdown Title: Add loyalty points to Customer Labels: ai-agent, enhancement Description: Add LoyaltyPoints field (int, default 0) to Customer entity. Requirements: - Update Domain/Entities/Customer.cs - Update Application/DTOs/CustomerDto.cs - Create EF Core migration - Add unit tests ``` ### 2. Trigger Agent ```bash make agent-process # Enter issue number: 138 ``` ### 3. Monitor Progress Issue comments show: ``` 🤖 AI Agent Started Phase 1/3: Analyzing task... 🤖 Execution Plan Summary: Add LoyaltyPoints field to Customer entity Steps: 1. MODIFY Domain/Entities/Customer.cs 2. MODIFY Application/DTOs/CustomerDto.cs 3. CREATE Migration file 4. CREATE Test file 🛠️ Phase 2/3: Generating code (4 files)... ✅ Step 1/4: modify Customer.cs ✅ Step 2/4: modify CustomerDto.cs ... 📦 Phase 3/3: Creating pull request... 🤖 Pull Request Created: #139 Branch: ai-agent/issue-138 Ready for review! ``` ### 4. Review PR PR includes: - Closes #138 - Comprehensive description - File changes summary - Testing checklist - Risk assessment ### 5. Merge Agent learns from successful merge for future tasks. --- ## 🧪 Testing Strategy ### Unit Tests - Agent logic (plan parsing, code generation) - Service mocks (GitHub, Cognee) - P

Microsoft says ungoverned AI agents could become corporate 'double agents.' Its fix costs $99 a month.

Microsoft today announced the general availability of Agent 365 and Microsoft 365 Enterprise 7, two products designed to bring security and governance to the rapidly growing population of AI agents operating inside the world's largest organizations. Both become available on May 1st, alongside Wave 3 of Microsoft 365 Copilot, which expands the company's agentic AI capabilities and adds model diversity from both OpenAI and Anthropic. Agent 365, priced at $15 per user per month, serves as what Microsoft calls the "control plane for agents" — a centralized system for IT, security, and business teams to observe, govern, and secure AI agents across an enterprise. Microsoft 365 Enterprise 7, dubbed the "Frontier Worker Suite," bundles Agent 365 with Microsoft 365 Copilot and the company's most advanced security stack into a single $99-per-user-per-month license. The timing is deliberate. AI agents have crossed from experimental prototypes into operational infrastructure, but the tools to monitor them have lagged behind. Microsoft is racing to close that gap before adversaries exploit it. "These agents are no longer experimental. We're seeing them deeply embedded in organizations, in the operational structure of these organizations, with people using them," Vasu Jakkal, corporate vice president of Microsoft Security, told VentureBeat in an exclusive interview. "At the same time, as the agents are scaling fast, some of the people and organizations have a visibility gap, and that visibility gap creates business risk." Over 80% of Fortune 500 companies use AI agents, but nearly a third aren't sanctioned The numbers behind the announcement tell a story of breakneck adoption outpacing oversight. According to Microsoft's Cyber Pulse report, published in February, more than 80 percent of Fortune 500 companies are actively using AI agents built with low-code and no-code tools. IDC projects 1.3 billion agents in circulation by 2028. And Microsoft, serving as its own first customer f

🚀 TUTORIAL #51: Advanced AI Integration - Pre-trained Models & APIs 🤖

# 🚀 **TUTORIAL #51: Advanced AI Integration - Pre-trained Models & APIs** 🤖 > **Priority:** Low | **Type:** Tutorial + Advanced AI | **Status:** 📋 Planning ## 🎯 **Tutorial Objective** Learn to integrate powerful pre-trained AI models and APIs into your FlowPro application, leveraging state-of-the-art language models while maintaining cost-effectiveness and performance. --- ## 📚 **What You'll Learn** ✅ **Pre-trained Model Integration:** Use existing AI models without training ✅ **API Integration:** Connect to OpenAI, Google AI, and other services ✅ **Cost Management:** Optimize API usage and control expenses ✅ **Performance Caching:** Cache responses to reduce API calls ✅ **Fallback Strategies:** Handle API failures gracefully ✅ **Context Management:** Maintain conversation context ✅ **Security Best Practices:** Protect API keys and user data ✅ **Real-time Processing:** Stream responses for better UX --- ## 🧠 **Prerequisites & Progression** ### **✅ Tutorial #11: Basic Pattern Recognition (Level 1)** ```javascript // Rule-based keyword matching if (text.includes('leaking')) return 'emergency_plumbing' ``` ### **✅ Tutorial #50: TensorFlow.js ML (Level 2)** ```javascript // Browser-based machine learning const model = tf.sequential() await model.fit(trainingData, trainingLabels) ``` ### **🚀 This Tutorial: Advanced AI Integration (Level 3)** ```javascript // Pre-trained models and APIs const response = await openai.chat.completions.create({ model: "gpt-3.5-turbo", messages: [{ role: "user", content: customerText }] }) ``` --- ## 🏗️ **Implementation Plan** ### **📁 Phase 1: API Setup & Configuration** #### **🔑 Environment Configuration:** ```javascript // config/ai.js export const AI_CONFIG = { openai: { apiKey: process.env.OPENAI_API_KEY, model: "gpt-3.5-turbo", maxTokens: 150, temperature: 0.3 }, googleAI: { apiKey: process.env.GOOGLE_AI_API_KEY, model: "gemini-pro", maxOutputTokens: 150 }, fallback: { enabled: true, useTensorFlow: true, useBasicPattern: true } } ``` #### **🛡️ Security Setup:** ```javascript // utils/security.js export const validateAPIKey = (key) => { // Validate API key format and permissions return key.startsWith('sk-') && key.length > 40 } export const sanitizeUserInput = (text) => { // Remove sensitive information before sending to AI return text.replace(/\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b/g, '[CARD]') .replace(/\b\d{3}[-\s]?\d{3}[-\s]?\d{4}\b/g, '[PHONE]') } ``` ### **📋 Phase 2: AI Service Integration** #### **🤖 OpenAI Integration:** ```javascript // services/ai/openaiService.js import OpenAI from 'openai' export class OpenAIService { constructor(config) { this.client = new OpenAI({ apiKey: config.apiKey }) this.config = config } async analyzeCustomerRequest(customerText) { const prompt = ` You are a plumbing service classifier. Analyze the customer's request and categorize it. Customer request: "${customerText}" Respond with JSON: { "category": "emergency_plumbing|water_heater_services|drain_cleaning_sewer|plumbing_repairs|gas_line_services|maintenance_inspection|bathroom_kitchen_fixtures|outdoor_drainage", "urgency": "high|medium|low", "confidence": 0.0-1.0, "reasoning": "brief explanation" } ` try { const response = await this.client.chat.completions.create({ model: this.config.model, messages: [{ role: "user", content: prompt }], max_tokens: this.config.maxTokens, temperature: this.config.temperature }) const result = JSON.parse(response.choices[0].message.content) return { success: true, data: result } } catch (error) { return { success: false, error: error.message } } } } ``` #### **🔍 Google AI Integration:** ```javascript // services/ai/googleAIService.js import { GoogleGenerativeAI } from "@google/generative-ai" export class GoogleAIService { constructor(config) { this.client = new GoogleGenerativeAI(config.apiKey) this.model = this.client.getGenerativeModel({ model: config.model }) } async analyzeCustomerRequest(customerText) { const prompt = `Classify this plumbing request: "${customerText}" Categories: emergency_plumbing, water_heater_services, drain_cleaning_sewer, plumbing_repairs, gas_line_services, maintenance_inspection, bathroom_kitchen_fixtures, outdoor_drainage Return JSON with: category, urgency (high/medium/low), confidence (0-1), reasoning` try { const result = await this.model.generateContent(prompt) const response = await result.response const text = response.text() return { success: true, data: JSON.parse(text) } } catch (error) { return { success: false, error: error.message } } } } ``` ### **🔄 Phase 3: Fallback & Caching System** #### **🗂️ Response Caching:** ```javascript // services/ai/cacheService.js export class AICach

Enterprise agentic AI requires a process layer most companies haven’t built

Presented by Celonis 85% of enterprises want to become agentic within three years — yet 76% admit their operations can’t support it. According to the Celonis 2026 Process Optimization Report, based on a survey of more than 1,600 global business leaders, organizations are aggressively pursuing AI-driven transformation. Yet most acknowledge that the foundational work — modernizing workflows, reducing process friction, and building operational resilience — remains unfinished. The ambition is clear. The infrastructure to execute on it is not. To act autonomously and effectively, AI agents need optimized, AI-ready processes and the process data and operational context that only comes from process intelligence. Without that, they’re guessing. And 82% of decision-makers believe AI will fail to deliver return on investment (ROI) if it doesn’t understand how the business runs. "The scale of the opportunity is truly remarkable: 89% of leaders see AI as their biggest competitive opportunity," says Patrick Thompson, global SVP of customer transformation. "That’s not a marginal finding. What’s interesting is the shift in the framing. Leaders are confident that AI will transform operations. The question now is how to fuel their ambitions with the right AI enablers." Explaining the gap between ambition and reality Right now, 85% of teams are using gen AI tools for everyday tasks, so the “will this work?” question is largely settled. The real question has shifted to: “Why isn’t it working the way we need it to?” And that’s a much harder problem, because it’s structural. It’s siloed teams. Systems that don’t talk to each other. AI that looks impressive in a demo but falters once it’s dropped into a real enterprise environment. That’s the wall companies are hitting. So, despite the overwhelming ambition, only 19% of organizations use multi-agent systems today. It all comes down to an operational readiness problem, Thompson says. "Nine in ten leaders are already using or exploring mul

Anthropic launches Claude Marketplace, giving enterprises access to Claude-powered tools from Replit, GitLab, Harvey and more

San Francisco startup Anthropic continues to ship new AI products and services at a blistering pace, despite a messy ongoing dispute with the U.S. Department of War. Today, the company announced Claude Marketplace, a new offering that lets enterprises with an existing Anthropic spend commitment apply part of it toward tools and applications powered by Anthropic's Claude models but made and offered by external partners including GitLab, Harvey, Lovable, Replit, Rogo and Snowflake. According to Anthropic’s Claude Marketplace FAQ, the program is designed to simplify procurement and consolidate AI spend. Anthropic says the Marketplace is now in limited preview and that enterprises interested in using it should reach out to their Anthropic account team to get started. For customers interested in the Marketplace, Anthropic says purchases made through it “count against a portion of your existing Anthropic commitment,” and that the company will manage invoicing for partner spend — meaning enterprises can use part of their existing Anthropic commitment to buy Claude-powered partner solutions without separately handling partner invoicing. In effect, Anthropic is positioning Claude Marketplace as a more centralized way for enterprises to procure certain Claude-powered partner tools. Yet, the whole point of Anthropic's Claude Code and Claude Cowork applications for many users was that they could shift enterprise spend and time away from current third-party software-as-a-service (Saas) apps and instead, they could "vibe code" new solutions or bespoke, AI-powered workflows. This idea is so pervasive that prior Claude integrations have on several recent occasions caused a major selloff in SaaS stocks after investors thought Claude could threaten the underlying companies and applications. Claude Marketplace seems to be pushing against that idea, suggesting current SaaS apps are still valuable and perhaps even more useful and appealing to enterprises with Claude integrated into them

New KV cache compaction technique cuts LLM memory 50x without accuracy loss

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working memory is stored. A new technique developed by researchers at MIT addresses this challenge with a fast compression method for the KV cache. The technique, called Attention Matching, manages to compact the context by up to 50x with very little loss in quality. While it is not the only memory compaction technique available, Attention Matching stands out for its execution speed and impressive information-preserving capabilities. The memory bottleneck of the KV cache Large language models generate their responses sequentially, one token at a time. To avoid recalculating the entire conversation history from scratch for every predicted word, the model stores a mathematical representation of every previous token it has processed, also known as the key and value pairs. This critical working memory is known as the KV cache. The KV cache scales with conversation length because the model is forced to retain these keys and values for all previous tokens in a given interaction. This consumes expensive hardware resources. "In practice, KV cache memory is the biggest bottleneck to serving models at ultra-long context," Adam Zweiger, co-author of the paper, told VentureBeat. "It caps concurrency, forces smaller batches, and/or requires more aggressive offloading." In modern enterprise use cases, such as analyzing massive legal contracts, maintaining multi-session customer dialogues, or running autonomous coding agents, the KV cache can balloon to many gigabytes of memory for a single user request. To solve this massive bottleneck, the AI industry has tried several strategies, but these methods fall short when deployed in enterprise environments where extreme compression is necessary. A class of technical fixes includes optimizing the KV cache by either evicting tokens the model deems less

Thanks to Trump's Iran War, US LNG Giants Could See $20 Billion in Monthly Windfall Profits

 From [declaring](https://www.commondreams.org/news/trump-energy-emergency-threat) an energy emergency and [ditching](https://www.commondreams.org/news/trump-withdraws-global-treaties) global climate initiatives to abducting the Venezuelan leader to [seize control](https://www.commondreams.org/news/venezuela-oil-sale-trump-donor) of the country's nationalized oil industry, President Donald Trump has taken various actions to serve his fossil fuel [donors](https://www.commondreams.org/news/big-oil-donations-trump) since returning to power last year. Now, his and Israel's war on Iran could soon lead to US liquefied natural gas giants pocketing tens of billions in windfall profits. "The Persian Gulf has some of the world's largest oil and gas producers," Oil Change International research co-director Lorne Stockman [explained](https://oilchange.org/blogs/trumps-war-on-iran-as-people-are-killed-big-oils-windfall-will-deepen-our-energy-affordability-crisis/) in a Tuesday blog post, "and a large proportion of that production, around 20% of global petroleum, must pass through a relatively narrow corridor controlled by Iran to reach global markets: the Strait of Hormuz," between the Persian Gulf and the Gulf of Oman. Stockman—whose advocacy group works to expose the costs of fossil fuels and facilitate a just transition to clean energy—noted that "crude oil, refined petroleum products, and liquefied natural gas (LNG) traverse the strait in [vast quantities every day](https://www.csis.org/analysis/how-war-iran-could-disrupt-energy-exports-strait-hormuz). But not since Saturday. With missiles, fighter jets, and drones circling, shipping has ground to a halt, and Iran [reportedly](https://www.aljazeera.com/news/2026/3/2/iran-says-will-attack-any-ship-trying-to-pass-through-strait-of-hormuz) threatened to close the strait by force on Monday." > As the conflict in the Persian Gulf continues, fossil fuel companies are preparing for record-breaking profits while billions of people face soaring energy bills and "energy poverty."We’re tired of a world where our energy system fuels war and destroys our climate. oilchange.org/blogs/trumps... > > [[image or embed]](https://bsky.app/profile/did:plc:dpuhovqi4tl6gdjpnqj5peay/post/3mg7yn5ltxc2h?ref_src=embed) > — 350.org ([@350.org](https://bsky.app/profile/did:plc:dpuhovqi4tl6gdjpnqj5peay?ref_src=embed)) [March 4, 2026 at 4:43 AM](https://bsky.app/profile/did:plc:dpuhovqi4tl6gdjpnqj5peay/post/3mg7yn5ltxc2h?ref_src=embed) Based on ship-tracking data from MarineTraffic, *Reuters* [estimated](https://www.reuters.com/business/energy/hormuz-shutdown-worsens-after-us-hits-iranian-warship-tankers-stranded-fifth-day-2026-03-04/) Wednesday that "at least 200 ships, including oil and liquefied natural gas tankers as well as cargo ships, remained at anchor in open waters off the coast of major Gulf producers including Iraq, Saudi Arabia, and Qatar," and "hundreds of other vessels remained outside Hormuz unable to reach ports." Stockman warned that "depending on how long the violence and its atrocious human toll continues—Trump said it [may take weeks](https://www.nytimes.com/2026/03/01/us/politics/trump-iran-war-interview.html) until his undefined objectives are achieved—this will have huge implications for energy markets. Oil and gas companies may achieve huge windfall profits in a year that previously looked far less lucrative for them, and billions of people could see their energy bills soar." Since Trump and Israeli Benjamin Netanyahu launched "Operation Epic Fury" on Saturday, over 1,000 people had been killed as of Wednesday, [according to](https://www.dropsitenews.com/p/iran-death-toll-1000-trump-kurds-iran-overthrow-lebanon-hezbollah-israel) the Iranian government, and oil prices have [surged](https://www.commondreams.org/news/iran-war-gas-prices)—highlighting how, as Greenpeace International executive director Mads Christensen [put it](https://www.commondreams.org/news/trump-iran-war-oil) earlier this week, "as long as our world runs on oil and gas, our peace, security and our pockets will always be at the mercy of geopolitics." Qatar exports about 20% of the global LNG supply, second only to the United States. All of that LNG goes through the Strait of Hormuz. An Iranian drone attack on Monday targeted Qatari LNG facilities, leading state-owned QatarEnergy to declare force majeure on exports. Two unnamed sources [told](https://www.reuters.com/business/energy/qatarenergy-declares-force-majeure-lng-shipments-2026-03-04/) *Reuters* that QE "will fully shut down gas liquefaction on Wednesday," and "it may take at least a month to return to normal production volumes." The Qatari shutdown is expected to boost the US LNG industry, wh

Is Flock just a poor US-centric copy of, globally active Genetec?

I've read all of Genetec's [customer stories](https://www.genetec.com/customer-stories/search) (the PDFs), and although I recognize these, as being Genetec marketing material (at least in part), they do contain insightful information, regarding implementation of surveillance systems; that is, from the perspective of a diverse palette of organisations. This palette primarily consists of: universities, school districts, ports, critical infrastructure providers, business to business companies, health care providers, real estate developers, gambling companies, (sports) venues, cities, public transportation services, airports, retailers, and foremost police departments. What most have in common, is the increasing scale at which they operate; setting in motion a search for IT-solutions, able to scale alongside organisational growth, and doing so in a cost-effective way. This entails: the centralisation of (previously "siloed") systems and departments, automatization of (previously time-consuming, or outright unmanageable) tasks, and proactive 'Data-Driven Decision-Making (DDDM)'; unlocking operational efficiencies and granular control over vast operations. Which is where Genetec introduces itself, primarily through [its partners](https://www.genetec.com/partners/partner-integration-hub?keywords) (including: hardware manufacturers, software solutions companies, system integrators, consultancy firms, etc.), often during an organisation's 'call for tender' or 'Request For Proposal (RFP)'; or it's recommended by other Genetec customers (including by law enforcement, to "community" partners: primarily businesses). The most recognizable partners, of the consortium-like construction, include: Axis Communications, Sony Corporation, Hanwha Vision, Bosch, NVIDIA, ASSA ABLOY, Intel, Pelco, Canon, Dell technologies, HID Global, FLIR Systems, Global Parking Solutions, and Seagate Technology. Alongside the Genetec-certified [hardware](https://www.genetec.com/supported-device-list) and software integrations (of which their partners' being actively co-marketed to customers), it also allows for custom integrations: through their 'Software Development Kits (SDKs)', and 'Application Programming Interfaces (APIs)'. So instead of single-vendor lock-in, organisations are effectively subject to multi-vendor lock-in (unless: spending resources, on custom integrations, is more cost-effective). Genetec's primary focus, lies on their extensive suite, of (specialized) software applications, deployed on: an on-site server, multiple (distributed) on-site servers (possibly federated: allowing for a centralized view over multiple implementations), in the "cloud" (i.e. someone else's server) as a '... as a Service' solution; or a combination of aforementioned (providing "cloud" flexibility). When using multiple applications, Genetec's 'Security Center' can unify all; meaning operators aren't required to switch between applications. And considering applications aren't limited to just camera surveillance, but also include: intrusion detection (intrusion panels, line-crossing cameras, panic switches, etc.), access control (electronic locks, access control readers (pin, card, tag, mobile, and/or biometric), door control modules, etc.), communication (intercoms, 'Public Address (PA)' systems, emergency stations, etc.) and ALPR (ALPR boom gates, gateless (license plate as a credential), enforcement vehicles, etc.); it allows for centralization of these systems (unless prohibited by strict IT policies). All of these technologies combined, primarily serve to: save on resources, protect assets, prevent losses, ensure operational continuity, and resolve disputes over: parking tickets, insurance claims (as a result of damages: suffered or caused on premise; potentially increasing premium), or even legal allegations ("increase the number of early guilty pleas"); all of course, under the guise of safety. Whether it be organisations individually, or "community" initiatives (often spearheaded by businesses, while citizens are left to follow); most circle back to previously outlined, financially-grounded motives. Resources include staff, who's function might become more versatile, or entirely obsolete (through efficiency gains), and might depend on events, reported by analytics (growing queues, areas requiring clean-up, crowd bottlenecks, etc.); meaning they too, are subject to this system: from onboarding ("minimise the time that elapses before they make a productive contribution") and throughout their career ("employee theft", "employee attendance", "agents' activities, collectively or individually", etc.). Previously, some organisations utilized analog cameras (having a recorder each), in which: a looping tape, would periodically overwrite previous recordings (minimizing retention periods: physically); which possbily caused quality degradations, sometimes to such a degree, footage could no longer serve as legal evidence (which too, is privacy-friendly).