[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-39dde3bc67a5d66f-replay-logs-fail-agents-use-vm-snapshots-instead-summary":3,"summaries-facets-categories":90,"summary-related-39dde3bc67a5d66f-replay-logs-fail-agents-use-vm-snapshots-instead-summary":3675},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":69,"path":70,"published_at":71,"question":48,"scraped_at":72,"seo":73,"sitemap":74,"source_id":75,"source_name":76,"source_type":77,"source_url":78,"stem":79,"tags":80,"thumbnail_url":85,"tldr":86,"tweet":87,"unknown_tags":88,"__hash__":89},"summaries\u002Fsummaries\u002F39dde3bc67a5d66f-replay-logs-fail-agents-use-vm-snapshots-instead-summary.md","Replay Logs Fail Agents: Use VM Snapshots Instead",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6744,1589,18772,0.0021208,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"replay-model-breaks-for-long-running-agents","Replay Model Breaks for Long-Running Agents",[22,23,24],"p",{},"Traditional replay durability—wrapping steps in a journal for replay on resume—built reliable async workflows like order processing without double-charging cards. It caches executed steps, provides audit trails, and enables resuming after human input or failures. But agents invert this: LLMs orchestrate tools in loops, turning every LLM call and tool into journal steps. After one turn, logs balloon; multi-turn interactions hit limits on entry count or size. Agent runtimes double every 4-7 months—from hours to days—making agents sessions, not transactions. Replay forces rigid, deterministic code outside steps, complicates versioning, and can't capture compute state like files, memory, or subprocesses (e.g., cloned repos, dev servers).",[17,26,28],{"id":27},"split-durability-context-logs-execution-snapshots","Split Durability: Context Logs + Execution Snapshots",[22,30,31],{},"Agents need two states: context (append-only log of system\u002Fuser messages, tool calls\u002Fresults, assistant responses) and execution (VM state: files, memory, subprocesses). Store context in databases, object storage, or distributed filesystems—durable across code versions, crashes, scales well. For execution, snapshot\u002Frestore the machine: save when idle (user lunches), restore on next message. This preserves Git clones, installed packages, datasets cheaply vs. always-on VMs. Recover selectively: snapshot for LLM outages (retry after 15min), restart execution from context log for machine bugs\u002Fcrashes. Agents force shift from 30-year stateless compute (CGI 1993 → LAMP → Node\u002FServerless) to stateful compute.",[17,33,35],{"id":34},"firecracker-snapshots-14mb-sub-second-ops","Firecracker Snapshots: 14MB, Sub-Second Ops",[22,37,38],{},"CRIU (2011) enabled userspace process checkpoint\u002Frestore via 'parasite' injection—transparent, container-compatible, but limited to single processes (no ffmpeg\u002FChrome), misses closed files, slow with registries. Firecracker microVMs snapshot entire machines, resuming subprocesses seamlessly. Naive 512MB RAM snapshots bloat storage\u002Ftransfer; compress with seekable format (decompress only accessed pages on restore), plus layering, yields 14MB tunable size. Results: snapshots \u003C1s, restores ~100-200ms. Open-source CLI 'fc-run' (docker-like) runs\u002Fsnaps\u002Frestores\u002Fforces VMs fast—15,000 starts\u002Fmin (30 FPS video equivalent), powers Trigger.dev's agent compute.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"AI Automation",null,"md",false,{"content_references":52,"triage":64},[53,57,59,61],{"type":54,"title":55,"context":56},"tool","CRIU","mentioned",{"type":54,"title":58,"context":56},"Firecracker microVMs",{"type":54,"title":60,"context":56},"fc-run",{"type":62,"title":63,"context":56},"other","IBM mainframe checkpoint\u002Frestore",{"relevance":65,"novelty":66,"quality":66,"actionability":66,"composite":67,"reasoning":68},5,4,4.35,"Category: AI Automation. The article provides a deep dive into the challenges of replay durability in AI agents and presents a practical solution using VM snapshots, which directly addresses the pain points of developers working with long-running AI agents. It offers actionable insights on how to implement this approach, making it highly relevant for the target audience.",true,"\u002Fsummaries\u002F39dde3bc67a5d66f-replay-logs-fail-agents-use-vm-snapshots-instead-summary","2026-05-10 20:00:06","2026-05-11 15:00:18",{"title":5,"description":40},{"loc":70},"39dde3bc67a5d66f","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=svCnShDvgQg","summaries\u002F39dde3bc67a5d66f-replay-logs-fail-agents-use-vm-snapshots-instead-summary",[81,82,83,84],"agents","open-source","ai-automation","devops-cloud","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FsvCnShDvgQg\u002Fhqdefault.jpg","Replay durability constrains agent code with growing logs; split into context logs (DB durable) and execution snapshots (14MB Firecracker VMs, \u003C1s save\u002F100ms restore) for multi-day sessions.","Conference talk by Trigger.dev CEO [Eric Allam](https:\u002F\u002Fx.com\u002Fmaverickdotdev) contrasting replay journaling (step-wise LLM\u002Ftool logs for recovery) with OS-level snapshots on Firecracker microVMs for preserving agent execution state like files and subprocesses, plus DB logs for context. Includes backend history and recovery 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Pitch Agent builds full branded pitch decks with comps, precedents, and LBOs over a weekend's work. Market Researcher outputs sector overviews, competitive landscapes, peer comps, and investment ideas. Earnings Reviewer processes call audio, press releases, and 10Qs to update models and draft notes. Model Builder runs live DCF, three-statement, and comps models in headless Excel (XLSX output). Middle-office agents like GL Reconciler trace ledger breaks for signoff, Month-End Closer manages accruals and variances, Valuation Reviewer applies firm templates to GP packages for LP reporting, Statement Auditor checks LP statements, and KYC Screener flags compliance gaps from onboarding docs. These replace $80k\u002Fyear junior roles in accounting and compliance, with human signoff required on all outputs.",[22,3694,3695],{},"Seven vertical skill bundles cover investment banking, equity research, private equity, wealth management, fund admin, operations, plus a core financial analysis bundle shared across agents. Eleven licensed MCP connectors integrate FactSet, S&P Global, Morningstar, Moody's, PitchBook, LSEG, Daloopa, Aiera, Chronograph, MT Newswires, and Egnyte—enabling data pulls only with valid subscriptions.",[17,3697,3699],{"id":3698},"plugin-architecture-enables-easy-customization","Plugin Architecture Enables Easy Customization",[22,3701,3702,3703,3707,3708,3711,3712,3715,3716,3719,3720,3723,3724,3727,3728,3731,3732,3735,3736,3739,3740,3743],{},"Each agent is a Claude Code plugin: an ",[3704,3705,3706],"code",{},"agent.md"," system prompt, ",[3704,3709,3710],{},"skills\u002F"," Markdown files, ",[3704,3713,3714],{},"commands\u002F"," slash commands, and ",[3704,3717,3718],{},"mcp.js"," for connector permissions. Skills sync from vertical plugins via ",[3704,3721,3722],{},"sync_agent_skills.py",". No build step—install in three commands on Claude's Cowork: add marketplace, install core financial analysis (includes connectors), cherry-pick agents\u002Fverticals. Deploy via ",[3704,3725,3726],{},"deploy_managed_agent.sh"," to ",[3704,3729,3730],{},"\u002Fv1\u002Fagents"," API or ",[3704,3733,3734],{},"orchestrate.py"," event loop for handoffs. Security limits: no bash, web fetch, or undeclared connectors; GitLeaks scans secrets for terminal credentials. May 5 restructure moved verticals to ",[3704,3737,3738],{},"plugins\u002Fvertical-plugins\u002F",", agents to ",[3704,3741,3742],{},"plugins\u002Fagent-plugins\u002F",", added managed agent cookbooks, fund admin vertical, and tool grants.",[17,3745,3747],{"id":3746},"trade-offs-and-fintech-impact","Trade-offs and Fintech Impact",[22,3749,3750],{},"Connectors require paid licenses ($25k\u002Fyear Bloomberg precedent), so not turnkey—tune prompts and swap data sources for production. Agents are templates, not finished products, but publish full skill ladders: prompts, conventions, audit checklists absent from vendors for 20 years. Forks (1.5k) and stars (11.4k, #1 Python trend day of launch) show traction from years of commits, partner plugins (LSEG, S&P Global). For fintech SaaS builders or agent frameworks, this sets 2026 reference ceiling—prospects can self-host analyst stacks, shifting sales conversations. Apache 2.0 license accelerates community extensions.",{"title":40,"searchDepth":41,"depth":41,"links":3752},[3753,3754,3755],{"id":3688,"depth":41,"text":3689},{"id":3698,"depth":41,"text":3699},{"id":3746,"depth":41,"text":3747},[47],{"content_references":3758,"triage":3768},[3759,3762,3765],{"type":54,"title":3760,"url":3761,"context":56},"Claude for Financial Services","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services",{"type":54,"title":3763,"url":3764,"context":56},"Cowork","https:\u002F\u002Fclaude.com\u002Fproduct\u002Fcowork",{"type":54,"title":3766,"url":3767,"context":56},"Managed Agents API","https:\u002F\u002Fdocs.claude.com\u002Fen\u002Fapi\u002Fmanaged-agents",{"relevance":66,"novelty":66,"quality":66,"actionability":66,"composite":66,"reasoning":3769},"Category: AI Automation. The article discusses the release of open-source AI agents that can perform specific financial analyst tasks, addressing the audience's need for practical AI applications in product development. It provides concrete examples of agent functionalities and integration, making it actionable for developers looking to implement similar solutions.","\u002Fsummaries\u002Fec6591609db819d3-anthropic-open-sources-wall-st-analyst-agents-summary","2026-05-08 17:24:49","2026-05-09 15:32:49",{"title":3678,"description":40},{"loc":3770},"ec6591609db819d3","AI Summaries (evaluation playlist)","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=E4GNZxJQqfM","summaries\u002Fec6591609db819d3-anthropic-open-sources-wall-st-analyst-agents-summary",[81,82,83],"Anthropic released 10 end-to-end Claude agents mimicking Goldman Sachs analyst roles, with prompts, checklists, 11 licensed data connectors, and 7 vertical bundles—democratizing workflows once locked behind $25k terminals and bank secrecy.","Walkthrough of Anthropic's [financial-services repo](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services), reading its README aloud and demoing the restructure into 10 finance-agent plugins (Pitch Agent, etc.) for workflows like IB decks and GL reconciliation, plus install via [Cowork](https:\u002F\u002Fclaude.com\u002Fproduct\u002Fcowork) and [Managed Agents API](https:\u002F\u002Fdocs.claude.com\u002Fen\u002Fapi\u002Fmanaged-agents). Covers architecture and recent commits but no hands-on runs.",[83],"WPKxqBiU9F-SwqNy4rXC6bS6TpNlS1bglsvFSrs6RTo",{"id":3786,"title":3787,"ai":3788,"body":3793,"categories":3829,"created_at":48,"date_modified":48,"description":3830,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":3831,"navigation":69,"path":3832,"published_at":3833,"question":48,"scraped_at":3834,"seo":3835,"sitemap":3836,"source_id":3837,"source_name":76,"source_type":77,"source_url":3838,"stem":3839,"tags":3840,"thumbnail_url":48,"tldr":3841,"tweet":48,"unknown_tags":3842,"__hash__":3843},"summaries\u002Fsummaries\u002F0780fb0ae6f63671-enterprise-registry-unifies-mcp-a2a-agents-at-scal-summary.md","Enterprise Registry Unifies MCP & A2A Agents at Scale",{"provider":7,"model":8,"input_tokens":3789,"output_tokens":3790,"processing_time_ms":3791,"cost_usd":3792},7146,1411,14889,0.00211205,{"type":14,"value":3794,"toc":3823},[3795,3799,3802,3806,3809,3813,3816,3820],[17,3796,3798],{"id":3797},"tackle-decentralized-ai-chaos-with-unified-registries","Tackle Decentralized AI Chaos with Unified Registries",[22,3800,3801],{},"Global teams across 26 countries, 20,000+ employees, and 10,000 stores at Amplifon faced reinvented security, fragmented infrastructure, and untraceable AI agents. Their Amplify program (launched Jan 2025) counters this via an AI gateway and three interconnected registries: MCP (tools\u002Ffunctions for LLMs), A2A (agent-to-agent), and Use Case. The gateway provides unified model access, Intra ID authentication, per-use-case budgeting (monthly\u002Fweekly), and centralized auditing\u002Fmonitoring. MCP registry extends the open-source spec with internal\u002Fcustom servers plus certified public ones, adding metadata like ownership (team\u002Fproject), environments (dev\u002Ftest\u002Fprod), auth models, cost attribution (tied to gateway budgets), and use case linkages. This enables impact analysis: trace server usage, audit trails, and lineage across assets. A2A registry uses agent cards (identity, endpoint, capabilities, modalities, auth) auto-published via CI\u002FCD on deployment, making agents self-discovering and interoperable. Use Case registry maps agents\u002Ftools\u002Fmodels to business contexts, tracking status, versions, lifecycle history, serving systems, and inter-use-case impacts for regulatory compliance and maintenance (e.g., LLM disruptions).",[17,3803,3805],{"id":3804},"standardize-development-with-production-ready-blueprints","Standardize Development with Production-Ready Blueprints",[22,3807,3808],{},"Provide GitHub template repos for MCP and A2A servers using FastAPI for consistent exposure, Dockerfiles, package management, built-in auth, and cost tracking. Integrate Langfuse for observability (tracing, evals, performance). A2A blueprint is framework-agnostic (LangChain, AutoGen, etc.) via ports\u002Finterfaces, letting teams focus on business logic while ensuring uniform interfaces. Wizards generate server.json (MCP) or agent cards (A2A) from forms, with JSON previews. Inspectors validate MCP servers or A2A compatibility in new tabs. Link repos to DevOps: tag a branch, GitHub Actions build\u002Fpush Docker images to artifact repo and auto-publish metadata to registry backend.",[17,3810,3812],{"id":3811},"enable-runtime-discovery-and-secure-routing","Enable Runtime Discovery and Secure Routing",[22,3814,3815],{},"Agents discover MCP\u002FA2A services dynamically: route via AI gateway proxies (MCP\u002FA2A proxies) that query registries for backend URLs, then forward with auth headers. This hides fragmentation—developers point to unified endpoints without knowing internals. Demo dashboard catalogs 6+ entities (use cases, MCPs, A2As), shows lineage graphs (e.g., 'Ticket Optimization' use case links to agents\u002Fmodels), and supports CRUD for use cases\u002Fassets.",[17,3817,3819],{"id":3818},"deliver-governance-traceability-and-scalability","Deliver Governance, Traceability, and Scalability",[22,3821,3822],{},"Outcome: Full catalog visibility across teams\u002Fcontinents; traceability from use cases to agents\u002Ftools\u002Fmodels; standardized blueprints\u002FCI\u002FCD reduce reinvention; prompt maintenance (e.g., model outages trigger targeted fixes via lineage). Production deployment imminent, with ongoing expansions. Contact speakers for blueprints or discussions.",{"title":40,"searchDepth":41,"depth":41,"links":3824},[3825,3826,3827,3828],{"id":3797,"depth":41,"text":3798},{"id":3804,"depth":41,"text":3805},{"id":3811,"depth":41,"text":3812},{"id":3818,"depth":41,"text":3819},[47],"As internal MCP servers and A2A agents explode in number, discovery and governance become critical challenges for production-grade AI systems. We'll demonstrate how we built an enterprise infrastructure to index MCP servers and A2A agents, and link them to relevant use cases. We'll show how moving from a fragmented environment to a searchable, metadata-rich registry transformed a chaotic development cycle into a standardized, scalable deployment process.\n \nIn this talk, we'll cover:\n- How we developed an internal private company MCP registry based on the open source specification\n- How we defined an A2A registry based on agent cards\n- How we achieved agent runtime discovery using an MCP server that exposes company A2A agents\n- How we linked A2A agent and MCP server template repositories to DevOps processes\n\nMauro Luchetti - AI CoE Manager, Quantyca\n\nI work as an AI Engineer and CoE Manager at Quantyca, where I focus on artificial intelligence solutions, data engineering, and cloud architectures, drawing on nearly 8 years of professional experience in the field. Over the years I've had the opportunity to work on projects involving generative AI, machine learning, data governance and data management, trying to combine hands-on technical skills with a broader strategic perspective. I enjoy sharing what I've learned with the teams I work with, contributing to collective growth in modern AI engineering practices.\n\nSocials:\nhttps:\u002F\u002Fwww.quantyca.it\u002F\n\nSlides:\nhttps:\u002F\u002Fquantyca-my.sharepoint.com\u002F:b:\u002Fg\u002Fpersonal\u002Fmauro_luchetti_quantyca_it\u002FIQBUCcMBzsAfSZtJXrCdaqV0AaUyDhifxP360fqCUupyaGc?e=S6ytoA",{},"\u002Fsummaries\u002F0780fb0ae6f63671-enterprise-registry-unifies-mcp-a2a-agents-at-scal-summary","2026-04-10 11:30:06","2026-04-10 15:01:09",{"title":3787,"description":3830},{"loc":3832},"0780fb0ae6f63671","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VXfRt_H-V08","summaries\u002F0780fb0ae6f63671-enterprise-registry-unifies-mcp-a2a-agents-at-scal-summary",[81,83,84],"Build private MCP and A2A registries enriched with enterprise metadata to enable discovery, governance, lineage, and standardized deployment across global teams building AI agents.",[83,84],"z7zTBUHJjGWvtKHrPJQmVkdijFaTQl9XB0iMQd_UZW8",{"id":3845,"title":3846,"ai":3847,"body":3852,"categories":4051,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":4052,"navigation":69,"path":4058,"published_at":48,"question":48,"scraped_at":4059,"seo":4060,"sitemap":4061,"source_id":4062,"source_name":4063,"source_type":3777,"source_url":4064,"stem":4065,"tags":4066,"thumbnail_url":48,"tldr":4067,"tweet":48,"unknown_tags":4068,"__hash__":4069},"summaries\u002Fsummaries\u002Ff4f23dfc53badb35-agentic-enterprise-new-it-architecture-for-scaling-summary.md","Agentic Enterprise: New IT Architecture for Scaling AI Agents",{"provider":7,"model":8,"input_tokens":3848,"output_tokens":3849,"processing_time_ms":3850,"cost_usd":3851},7721,2542,25815,0.00279255,{"type":14,"value":3853,"toc":4044},[3854,3858,3861,3864,3867,3871,3874,3881,3887,3893,3896,3899,3903,3906,3958,3961,3964,3968,3971,3977,3983,3989,3995,3998,4001,4007,4011],[17,3855,3857],{"id":3856},"traditional-it-fails-at-agentic-scale","Traditional IT Fails at Agentic Scale",[22,3859,3860],{},"Current enterprise IT—built around Infrastructure, Data, Integration, Application, and Experience layers with Security and Ops cross-cutting—assumes human-driven intelligence. Humans access apps to process data, apply logic, and execute workflows. This works for narrow AI pilots but crumbles under widespread AI agents that sense, reason, act autonomously, and collaborate. Key gaps: no shared AI model management, no dedicated agent hosting\u002Freasoning\u002Fmemory ops, siloed data lacking semantic context for cross-agent reasoning, and rigid orchestration for linear workflows unfit for dynamic, multi-step agent-human processes.",[22,3862,3863],{},"Agents evolve rapidly: smarter multi-modal LLMs, better memory\u002Fself-reflection\u002Flearning from feedback, richer inter-agent\u002Ftool standards (e.g., Model Context Protocol, Agent2Agent). Scaling them demands future-proofing against non-determinism, cascading risks at machine speed, and exploding inference costs. Without evolution, enterprises stay stuck with info silos, manual drudgery, misaligned incentives, and broken strategy-outcome loops—yielding poor CX, inefficient ops, and growth misses.",[22,3865,3866],{},"\"The traditional IT architecture was designed for a paradigm where the enterprise’s intelligence resided with human workers... It is not designed for a paradigm where AI agents can reason and take actions for certain use cases previously done by humans.\" This quote from Salesforce Architects nails why bolt-on AI fails: it ignores agents' unique needs like model swapping (foundation vs. domain-specific), elastic scaling, and semantic reasoning over disparate data.",[17,3868,3870],{"id":3869},"unlocking-business-value-through-agentic-capabilities","Unlocking Business Value Through Agentic Capabilities",[22,3872,3873],{},"AI agents + humans create an augmented workforce for three pillars: innovation\u002Fgrowth, operating excellence, and resilience.",[22,3875,3876,3880],{},[3877,3878,3879],"strong",{},"Innovation:"," Agents boost human productivity by automating repetition (always-on scale\u002Fspeed) and enable adaptive skills via observable reasoning—dynamic learning\u002Fdeploying improvements. Example: Wealth mgmt firm deploys portfolio-monitoring agent that flags review moments, preps advisor plans (adapting to news), enabling proactive, personalized CX at scale—strengthening ties, surfacing opportunities.",[22,3882,3883,3886],{},[3877,3884,3885],{},"Resilience:"," Elastic capacity handles workload surges sans hiring lags\u002Fcosts; predictive ops anticipates\u002Fmitigates risks 24\u002F7. Example: Data governance agent scans regs, classifies sensitive data, applies policies, routes access exceptions to humans—slashing compliance risk while trusting data use.",[22,3888,3889,3892],{},[3877,3890,3891],{},"Excellence:"," Autonomous multi-step execution at machine speed (humans-in-loop); transboundary orchestration breaks silos. Example: Retail agent crafts\u002Fadjusts campaigns from trends, collaborates cross-team, generates collateral, executes multi-channel with real-time feedback.",[22,3894,3895],{},"These aren't hypotheticals— they're concrete paths to step-change productivity\u002Fagility. Tradeoff: unleashing autonomy risks misalignment without governance; agents amplify both upsides (speed) and downsides (hallucinations, biases) at enterprise scale.",[22,3897,3898],{},"\"With this new AI-augmented workforce, an organization can foster innovation for growth, drive operating excellence, and build enterprise resilience with several types of new business capabilities.\" Salesforce emphasizes integration over replacement: agents handle routine\u002Fscale, humans creativity\u002Fambiguity.",[17,3900,3902],{"id":3901},"core-design-principles-for-agentic-it","Core Design Principles for Agentic IT",[22,3904,3905],{},"To build this, follow eight principles prioritizing agents as default actors:",[3907,3908,3909,3916,3922,3928,3934,3940,3946,3952],"ol",{},[3910,3911,3912,3915],"li",{},[3877,3913,3914],{},"Composability\u002FModularity:"," Modular components, std interfaces (e.g., APIs\u002Fevents) for dynamic workflow assembly—abstraction hides complexity.",[3910,3917,3918,3921],{},[3877,3919,3920],{},"Data\u002FSemantic First:"," Data-as-product with quality\u002Flineage\u002Fgovernance; shared ontology for agent reasoning across silos.",[3910,3923,3924,3927],{},[3877,3925,3926],{},"Embedded Observability:"," End-to-end tracing\u002Fexplainability for reasoning\u002Fbehavior\u002FKPIs; FinOps\u002Fsustainability; non-determinism demands auditability.",[3910,3929,3930,3933],{},[3877,3931,3932],{},"Trust-Throughout:"," Intent-based granular perms, red-teaming, CVE scans, output validation (bias\u002Ftoxicity), verifiable trails.",[3910,3935,3936,3939],{},[3877,3937,3938],{},"Agent-First w\u002F Oversight:"," Agents default; humans monitor\u002Fintervene\u002Foverride; self-reflection for low-confidence escalations.",[3910,3941,3942,3945],{},[3877,3943,3944],{},"Reactive\u002FMultimodal:"," Handle all inputs (voice\u002Ftext\u002Fvisual\u002Fevents\u002Fstreams); event-driven\u002Freal-time.",[3910,3947,3948,3951],{},[3877,3949,3950],{},"AI-Ready Infra:"," Elastic\u002Fredundant scaling, ML pipelines integrated, data residency.",[3910,3953,3954,3957],{},[3877,3955,3956],{},"Open Ecosystem:"," Std protocols avoid lock-in.",[22,3959,3960],{},"Tradeoffs explicit: modularity boosts flexibility but needs ironclad interfaces; observability adds overhead but prevents trust erosion; agent-first accelerates but mandates overrides to curb risks.",[22,3962,3963],{},"\"Observability is paramount to ensure AI agents can operate in a trusted, compliant, and auditable manner with human oversight.\" This underscores non-determinism's core challenge—black-box agents cascade failures invisibly.",[17,3965,3967],{"id":3966},"four-new-layers-transform-the-stack","Four New Layers Transform the Stack",[22,3969,3970],{},"Evolve 5+2 traditional layers to 11 by adding Agentic, Semantic, AI\u002FML, Enterprise Orchestration. Existing layers adapt: Apps → composable services; Data → lakehouse\u002Ffabric\u002Fmesh.",[22,3972,3973,3976],{},[3877,3974,3975],{},"Agentic Layer:"," Agent dev\u002Fmanagement—planning, reasoning, memory, tools, state\u002Flifecycle. Std protocols for app\u002Fdata interop, agent-agent collab. Separates agent ops from apps to avoid complexity.",[22,3978,3979,3982],{},[3877,3980,3981],{},"Semantic Layer:"," Enterprise ontology\u002Fentities\u002Frelations; translates NL queries to precise data fetches, harmonizes results. Powers multi-agent complexity beyond catalogs.",[22,3984,3985,3988],{},[3877,3986,3987],{},"AI\u002FML Layer:"," Centralized model lifecycle (internal\u002Fexternal)—train\u002Fdeploy\u002Fgovern\u002Frisk. Models as shared services, not app-embedded; trust\u002Fsafety\u002Fcompliance gateways.",[22,3990,3991,3994],{},[3877,3992,3993],{},"Enterprise Orchestration Layer:"," Blended model—local agent choreography (MCP\u002FA2A) + central oversight for multi-step\u002Fdynamic processes spanning agents\u002Fhumans\u002Ftools\u002Fsystems. Machine-legible processes mix design-time deterministic + runtime agent-decided steps. Captures agent observability to build digital twins\u002Fprocess models\u002Fplaybooks. Ensures KPIs, integrity, rollbacks, policy enforcement (policy-as-code), human gates.",[22,3996,3997],{},"Example: Sales strategy\u002Fonboarding—orchestrates interdeps, bounds autonomy, tracks stages. Tradeoff: Decentralized risks drift; blended adds governance without stifling speed.",[22,3999,4000],{},"\"The detailed observability of AI agents’ activities... enables capturing, documenting, and integrating dynamic, previously unstructured work... creating a holistic digital twin.\" Highlights observability's dual role: trust + optimization.",[22,4002,4003,4006],{},[3877,4004,4005],{},"Experience Layer Evolution (for completeness):"," Multimodal UIs, conversational AI, proactive notifications, omnichannel, dynamic personalization—agents handoff intents, show attributions.",[17,4008,4010],{"id":4009},"key-takeaways","Key Takeaways",[4012,4013,4014,4017,4020,4023,4026,4029,4032,4035,4038,4041],"ul",{},[3910,4015,4016],{},"Audit your IT stack against agent gaps: model mgmt, semantic data, agent ops, dynamic orchestration—pilot one layer at a time.",[3910,4018,4019],{},"Prioritize Semantic + Orchestration for cross-silo reasoning\u002Fworkflows; they're the biggest unlocks for scale.",[3910,4021,4022],{},"Embed observability\u002Ftrust from day zero—non-determinism makes post-hoc fixes impossible.",[3910,4024,4025],{},"Use open protocols (MCP, A2A) for interoperability; vendor lock-in kills agent ecosystems.",[3910,4027,4028],{},"Start with business caps like elastic capacity or autonomous processes—map to layers for ROI.",[3910,4030,4031],{},"Design for blended human-AI: agents default, but escalations\u002Foverrides mandatory.",[3910,4033,4034],{},"Build data as product first—ontology > raw access for agent intelligence.",[3910,4036,4037],{},"Target 3-5 year horizon: agents get smarter; architecture must elastic-scale inference.",[3910,4039,4040],{},"Measure success via KPIs in orchestration: cycle time, compliance breaches, cost per workflow.",[3910,4042,4043],{},"Avoid embedding agents in apps—dedicated layers prevent tech debt.",{"title":40,"searchDepth":41,"depth":41,"links":4045},[4046,4047,4048,4049,4050],{"id":3856,"depth":41,"text":3857},{"id":3869,"depth":41,"text":3870},{"id":3901,"depth":41,"text":3902},{"id":3966,"depth":41,"text":3967},{"id":4009,"depth":41,"text":4010},[47,414],{"content_references":4053,"triage":4054},[],{"relevance":65,"novelty":66,"quality":66,"actionability":4055,"composite":4056,"reasoning":4057},3,4.15,"Category: AI Automation. The article provides a deep dive into the limitations of traditional IT architectures in scaling AI agents, addressing a core pain point for product builders looking to integrate AI effectively. It offers insights into necessary architectural changes, although it lacks specific actionable steps for implementation.","\u002Fsummaries\u002Ff4f23dfc53badb35-agentic-enterprise-new-it-architecture-for-scaling-summary","2026-04-14 14:30:50",{"title":3846,"description":40},{"loc":4058},"f4f23dfc53badb35","__oneoff__","https:\u002F\u002Farchitect.salesforce.com\u002Fdocs\u002Farchitect\u002Ffundamentals\u002Fguide\u002Fagentic-enterprise-it-architecture","summaries\u002Ff4f23dfc53badb35-agentic-enterprise-new-it-architecture-for-scaling-summary",[81,83,84],"Traditional IT can't scale AI agents; add Agentic, Semantic, AI\u002FML, and Orchestration layers to enable innovation, resilience, and efficiency via composable, observable systems.",[83,84],"Oag8nRqDvxmq_gOlBRqQmwLfPx9GCBptrSOQefjimO8",{"id":4071,"title":4072,"ai":4073,"body":4078,"categories":4421,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":4422,"navigation":69,"path":4433,"published_at":4434,"question":48,"scraped_at":4435,"seo":4436,"sitemap":4437,"source_id":4438,"source_name":4439,"source_type":3777,"source_url":4440,"stem":4441,"tags":4442,"thumbnail_url":48,"tldr":4443,"tweet":48,"unknown_tags":4444,"__hash__":4445},"summaries\u002Fsummaries\u002F6ca953036b6b121d-manual-deployment-unlocks-foundry-hosted-agents-summary.md","Manual Deployment Unlocks Foundry Hosted Agents",{"provider":7,"model":8,"input_tokens":4074,"output_tokens":4075,"processing_time_ms":4076,"cost_usd":4077},5840,1926,21198,0.0021085,{"type":14,"value":4079,"toc":4416},[4080,4084,4087,4102,4106,4121,4131,4268,4282,4297,4301,4308,4402,4412],[17,4081,4083],{"id":4082},"avoid-azd-black-box-for-reliable-deployments","Avoid azd Black Box for Reliable Deployments",[22,4085,4086],{},"Azure Developer CLI (azd) templates for Foundry hosted agents fail in preview due to opaque internals, making troubleshooting impossible without understanding the underlying cloud-native container model. Instead, deploy manually using basic Azure CLI tools and portal steps: create infrastructure first (ACR, image, Foundry Project, RBAC), then the agent second. This gives full control over orchestration and harness, unlike prompt agents.",[22,4088,4089,4090,4093,4094,4097,4098,4101],{},"Install azd extension only for init (",[3704,4091,4092],{},"azd ai agent init -m \u003Cagent-definition-url>","), but skip ",[3704,4095,4096],{},"azd up",". Use ",[3704,4099,4100],{},"az acr build"," for cloud-based Docker builds without local Docker.",[17,4103,4105],{"id":4104},"container-workflow-acr-image-to-running-agent","Container Workflow: ACR Image to Running Agent",[22,4107,4108,4109,4112,4113,4116,4117,4120],{},"Build agent as Docker image in Azure Container Registry (ACR): name it ",[3704,4110,4111],{},"debugagent",", push ",[3704,4114,4115],{},"my-hosted-agent:latest"," with ",[3704,4118,4119],{},"az acr build --registry debugagent --image my-hosted-agent:latest --platform linux\u002Famd64 --file .\u002Fsrc\u002Fdebug-agent\u002FDockerfile .\u002Fsrc\u002Fdebug-agent",".",[22,4122,4123,4124,4127,4128,4120],{},"Agent code uses Microsoft Agent Framework: reads config from env vars like ",[3704,4125,4126],{},"AZURE_AI_PROJECT_ENDPOINT"," and ",[3704,4129,4130],{},"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME",[4132,4133,4137],"pre",{"className":4134,"code":4135,"language":4136,"meta":40,"style":40},"language-python shiki shiki-themes github-light github-dark","import os\nfrom azure.identity.aio import DefaultAzureCredential\nfrom dotenv import load_dotenv\nfrom agent_framework.azure import AzureAIClient\nfrom azure.ai.agentserver.agentframework import from_agent_framework\n\nload_dotenv(override=False)\nasync def main():\n    credential = DefaultAzureCredential()\n    async with AzureAIClient(\n        project_endpoint=os.getenv(\"AZURE_AI_PROJECT_ENDPOINT\"),\n        model_deployment_name=os.getenv(\"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"),\n        credential=credential,\n    ).as_agent(\n        name=\"Assistant\",\n        instructions=\"You are a helpful assistant. Answer questions concisely and accurately.\",\n    ) as agent:\n        await from_agent_framework(agent).run_async()\n\nif __name__ == \"__main__\":\n    import asyncio\n    asyncio.run(main())\n","python",[3704,4138,4139,4147,4152,4157,4162,4167,4173,4179,4185,4191,4197,4203,4209,4215,4221,4227,4233,4239,4245,4250,4256,4262],{"__ignoreMap":40},[4140,4141,4144],"span",{"class":4142,"line":4143},"line",1,[4140,4145,4146],{},"import os\n",[4140,4148,4149],{"class":4142,"line":41},[4140,4150,4151],{},"from azure.identity.aio import DefaultAzureCredential\n",[4140,4153,4154],{"class":4142,"line":4055},[4140,4155,4156],{},"from dotenv import load_dotenv\n",[4140,4158,4159],{"class":4142,"line":66},[4140,4160,4161],{},"from agent_framework.azure import AzureAIClient\n",[4140,4163,4164],{"class":4142,"line":65},[4140,4165,4166],{},"from azure.ai.agentserver.agentframework import from_agent_framework\n",[4140,4168,4170],{"class":4142,"line":4169},6,[4140,4171,4172],{"emptyLinePlaceholder":69},"\n",[4140,4174,4176],{"class":4142,"line":4175},7,[4140,4177,4178],{},"load_dotenv(override=False)\n",[4140,4180,4182],{"class":4142,"line":4181},8,[4140,4183,4184],{},"async def main():\n",[4140,4186,4188],{"class":4142,"line":4187},9,[4140,4189,4190],{},"    credential = DefaultAzureCredential()\n",[4140,4192,4194],{"class":4142,"line":4193},10,[4140,4195,4196],{},"    async with AzureAIClient(\n",[4140,4198,4200],{"class":4142,"line":4199},11,[4140,4201,4202],{},"        project_endpoint=os.getenv(\"AZURE_AI_PROJECT_ENDPOINT\"),\n",[4140,4204,4206],{"class":4142,"line":4205},12,[4140,4207,4208],{},"        model_deployment_name=os.getenv(\"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"),\n",[4140,4210,4212],{"class":4142,"line":4211},13,[4140,4213,4214],{},"        credential=credential,\n",[4140,4216,4218],{"class":4142,"line":4217},14,[4140,4219,4220],{},"    ).as_agent(\n",[4140,4222,4224],{"class":4142,"line":4223},15,[4140,4225,4226],{},"        name=\"Assistant\",\n",[4140,4228,4230],{"class":4142,"line":4229},16,[4140,4231,4232],{},"        instructions=\"You are a helpful assistant. Answer questions concisely and accurately.\",\n",[4140,4234,4236],{"class":4142,"line":4235},17,[4140,4237,4238],{},"    ) as agent:\n",[4140,4240,4242],{"class":4142,"line":4241},18,[4140,4243,4244],{},"        await from_agent_framework(agent).run_async()\n",[4140,4246,4248],{"class":4142,"line":4247},19,[4140,4249,4172],{"emptyLinePlaceholder":69},[4140,4251,4253],{"class":4142,"line":4252},20,[4140,4254,4255],{},"if __name__ == \"__main__\":\n",[4140,4257,4259],{"class":4142,"line":4258},21,[4140,4260,4261],{},"    import asyncio\n",[4140,4263,4265],{"class":4142,"line":4264},22,[4140,4266,4267],{},"    asyncio.run(main())\n",[22,4269,4270,4271,4274,4275,4278,4279,4120],{},"Dockerfile: ",[3704,4272,4273],{},"FROM python:3.12-slim",", copy code, ",[3704,4276,4277],{},"pip install -r requirements.txt"," if present, expose 8088, ",[3704,4280,4281],{},"CMD [\"python\", \"main.py\"]",[22,4283,4284,4285,4288,4289,4292,4293,4296],{},"Create Foundry Project, deploy ",[3704,4286,4287],{},"gpt-4.1"," model. Assign ",[3704,4290,4291],{},"AcrPull"," or ",[3704,4294,4295],{},"Container Registry Repository Reader"," RBAC from ACR to Foundry Project's managed identity for image pull.",[17,4298,4300],{"id":4299},"sdk-agent-creation-with-rbac-secures-access","SDK Agent Creation with RBAC Secures Access",[22,4302,4303,4304,4307],{},"Use ",[3704,4305,4306],{},"azure.ai.projects"," SDK to create agent:",[4132,4309,4311],{"className":4134,"code":4310,"language":4136,"meta":40,"style":40},"from azure.ai.projects import AIProjectClient\nfrom azure.ai.projects.models import HostedAgentDefinition, ProtocolVersionRecord, AgentProtocol\n\nproject_client = AIProjectClient(endpoint=FOUNDRY_ENDPOINT, credential=credential, allow_preview=True)\nagent = project_client.agents.create_version(\n    agent_name=\"my-hosted-agent\",\n    definition=HostedAgentDefinition(\n        kind=\"hosted\",\n        container_protocol_versions=[ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version=\"1.0.0\")],\n        cpu=\"0.25\",\n        memory=\"0.5Gi\",\n        image=\"debugagent.azurecr.io\u002Fmy-hosted-agent:latest\",\n        environment_variables={\n            \"AZURE_AI_PROJECT_ENDPOINT\": FOUNDRY_ENDPOINT,\n            \"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\": MODEL_DEPLOYMENT,\n        }\n    )\n)\n",[3704,4312,4313,4318,4323,4327,4332,4337,4342,4347,4352,4357,4362,4367,4372,4377,4382,4387,4392,4397],{"__ignoreMap":40},[4140,4314,4315],{"class":4142,"line":4143},[4140,4316,4317],{},"from azure.ai.projects import AIProjectClient\n",[4140,4319,4320],{"class":4142,"line":41},[4140,4321,4322],{},"from azure.ai.projects.models import HostedAgentDefinition, ProtocolVersionRecord, AgentProtocol\n",[4140,4324,4325],{"class":4142,"line":4055},[4140,4326,4172],{"emptyLinePlaceholder":69},[4140,4328,4329],{"class":4142,"line":66},[4140,4330,4331],{},"project_client = AIProjectClient(endpoint=FOUNDRY_ENDPOINT, credential=credential, allow_preview=True)\n",[4140,4333,4334],{"class":4142,"line":65},[4140,4335,4336],{},"agent = project_client.agents.create_version(\n",[4140,4338,4339],{"class":4142,"line":4169},[4140,4340,4341],{},"    agent_name=\"my-hosted-agent\",\n",[4140,4343,4344],{"class":4142,"line":4175},[4140,4345,4346],{},"    definition=HostedAgentDefinition(\n",[4140,4348,4349],{"class":4142,"line":4181},[4140,4350,4351],{},"        kind=\"hosted\",\n",[4140,4353,4354],{"class":4142,"line":4187},[4140,4355,4356],{},"        container_protocol_versions=[ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version=\"1.0.0\")],\n",[4140,4358,4359],{"class":4142,"line":4193},[4140,4360,4361],{},"        cpu=\"0.25\",\n",[4140,4363,4364],{"class":4142,"line":4199},[4140,4365,4366],{},"        memory=\"0.5Gi\",\n",[4140,4368,4369],{"class":4142,"line":4205},[4140,4370,4371],{},"        image=\"debugagent.azurecr.io\u002Fmy-hosted-agent:latest\",\n",[4140,4373,4374],{"class":4142,"line":4211},[4140,4375,4376],{},"        environment_variables={\n",[4140,4378,4379],{"class":4142,"line":4217},[4140,4380,4381],{},"            \"AZURE_AI_PROJECT_ENDPOINT\": FOUNDRY_ENDPOINT,\n",[4140,4383,4384],{"class":4142,"line":4223},[4140,4385,4386],{},"            \"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\": MODEL_DEPLOYMENT,\n",[4140,4388,4389],{"class":4142,"line":4229},[4140,4390,4391],{},"        }\n",[4140,4393,4394],{"class":4142,"line":4235},[4140,4395,4396],{},"    )\n",[4140,4398,4399],{"class":4142,"line":4241},[4140,4400,4401],{},")\n",[22,4403,4404,4405,4292,4408,4411],{},"Agent gets Entra ID (Agent ID). Assign ",[3704,4406,4407],{},"Azure AI User",[3704,4409,4410],{},"Azure AI Project Manager"," RBAC at Foundry Project scope for model access. Result: production-grade hosted agent runs with full control.",[4413,4414,4415],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":40,"searchDepth":41,"depth":41,"links":4417},[4418,4419,4420],{"id":4082,"depth":41,"text":4083},{"id":4104,"depth":41,"text":4105},{"id":4299,"depth":41,"text":4300},[414],{"content_references":4423,"triage":4430},[4424,4426,4428],{"type":54,"title":4425,"context":56},"Azure Developer CLI (azd)",{"type":54,"title":4427,"context":56},"Azure AI Projects SDK",{"type":54,"title":4429,"context":56},"Microsoft Agent Framework",{"relevance":65,"novelty":66,"quality":66,"actionability":65,"composite":4431,"reasoning":4432},4.55,"Category: AI Automation. The article provides a detailed, practical guide on deploying Foundry hosted agents using Azure tools, addressing a specific pain point for developers overwhelmed by deployment complexities. It includes step-by-step instructions and code snippets that the audience can directly implement.","\u002Fsummaries\u002F6ca953036b6b121d-manual-deployment-unlocks-foundry-hosted-agents-summary","2026-05-06 17:04:31","2026-05-07 11:23:31",{"title":4072,"description":40},{"loc":4433},"6ca953036b6b121d","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fbreaking-open-the-black-box-the-real-deployment-path-for-microsoft-foundry-hosted-agents-d93cde1ede72?source=rss----5517fd7b58a6---4","summaries\u002F6ca953036b6b121d-manual-deployment-unlocks-foundry-hosted-agents-summary",[81,84,83],"Deploy Foundry hosted agents by building container images in ACR, setting up Foundry Project with RBAC, creating via Azure SDK with env vars and resources (cpu=0.25, mem=0.5Gi), then assigning Azure AI User RBAC to Agent ID—avoids azd preview failures.",[84,83],"BGMpgyzF5GQZoZAWWEj9Q0dVoUTGZ0BD8nO1GLLy2F8"]