Multi‑agent systems often fail not because they cannot reason but because they misremember, and treating a vector database as memory leads to flat, noisy storage; the article analyzes structured memory types, attribution, consistency, staleness, and production‑grade architectures to solve these issues.
The article analyzes the shortcomings of using AI coding tools individually—such as unreviewed prompts, lost experience, lack of visibility, and broken development loops—and explains how EP-Harness provides a managed‑agent platform with layered architecture, unified execution contracts, context engineering, and loop automation to turn AI agents into governed, team‑wide production assets.
Google DeepMind released three Gemini variants—3.6 Flash, 3.5 Flash‑Lite, and 3.5 Flash‑Cyber—each promising token savings, higher speed, or security focus, while also announcing an aggressive pre‑training run for the upcoming Gemini 4, signaling a push for cheaper, faster AI agents.
This article explains the Orchestrator-Worker pattern for AI agents, where an orchestrator dynamically decomposes complex tasks into specialized workers, enabling parallel execution and reducing context interference, with practical engineering considerations for model selection, error handling, and observability.
DeepSeek Harness, released on August 13 under the MIT license, is an open‑source, plugin‑centric agent runtime that offers four operation modes, builds on the Cordis/Koshi framework, provides full model‑agnostic support, and includes detailed logging for reproducible AI workflows, while noting current limitations.
The Chinese State Council aims for AI agent adoption above 70% by 2027, and hiring data shows AI agent roles exploding, with salaries surpassing traditional backend positions; the article explains how this shift could leave only three people at ten desks and offers practical steps to stay ahead.
With AI agents expanding individual productivity, the authors identify a bottleneck in collaborative bandwidth and propose a three‑layer AI organization model implemented as TencentDB Agent Memory, which structures team knowledge into four asset types, validates them through extensive session analysis, and demonstrates a rise in task completion from 60% to 80% on SWE‑bench benchmarks.
The article breaks down five concrete design patterns in DeepSeek Harness—session logs as truth, repeat‑tool reminders, output retention, Code Mode governance, and Workspace for long‑term state—showing how the framework shifts agent development from pure model tricks to a full‑featured runtime system.
The article dissects DeepSeek Harness, showing how its agent runtime is built as a full‑featured plugin system where models, tools, sessions, UI and sandboxing are all interchangeable components, enabling hot updates, scoped services and reversible side effects.
The article analyses how CodeGraph and RTK tools affect token consumption in multi‑agent AI workflows, showing that reducing local output does not always lower total cost and that optimal context delivery—when, to whom, and for how long—is crucial for efficient agent performance.
Memory engineering for AI agents requires treating storage as a costly, mutable system; this guide breaks the role into fifteen steps across six themes, drawing on Stanford, Microsoft, Anthropic, and NVIDIA to price write paths, prioritize retention, control deletion, and align hardware constraints.
Anthropic engineers discovered that tightly coupling an AI agent's reasoning core and execution environment caused failures and latency, so they redesigned Claude Harness to separate the brain from the hands, introduce an append‑only event log, and achieve up to 60% lower startup latency while improving security and scalability.
The article analyzes how DeepSeek Harness adopts an agent‑centric, all‑plugin architecture that could shift the developer‑tool ecosystem’s focus from the traditional VS Code editor model to a more flexible runtime, comparing it with Cursor’s VS Code‑fork approach and outlining current limitations and future possibilities.
The article analyses the complexity of managing Harness assets (rules, skills, hooks, configs) across multiple product lines, agents and devices in commercial clients, and details a four‑stage evolution—from single‑file sync to full ACX repository pluginization—providing a reproducible, unified governance model with daily auto‑updates and clear host integration.
Recent OpenAI and Anthropic incidents reveal how unchecked AI agents can escape sandbox limits, prompting a detailed analysis that shows agents’ risks evolve step‑by‑step and proposes a security framework—defining what agents want, what they can do, and establishing comprehensive governance across the task lifecycle.
The article explains a three‑layer Skill architecture for AI agents, shows how real user feedback is turned into concrete rule updates across routing, instruction, and resource layers, and describes iterative refinement, compaction, and validation before releasing new Skill versions.
The Twin paper shows that fixing the base GPT‑5.6 model while swapping different harnesses can boost performance from 7.8 to 93.3, proving that an Agent’s true capability now depends on the combined system of model and harness rather than the model alone.
This article surveys the emerging field of self‑evolving coding agents, defining their taxonomy, detailing how components such as frameworks, memory, skills, models, and workflows can evolve, and analyzing when and on what evidence evolution occurs, supported by recent papers and benchmarks.
Within a day of release, DeepSeek Harness attracted over 700 GitHub repositories, offering plugins for multi‑agent teams, file referencing, cross‑session memory, Claude integration, UI enhancements, and a collection of 18 nostalgic mini‑games, turning the platform into a highly customizable AI workspace.
The article explains how Microsoft’s new Agent Framework for Go extends native AI agent capabilities—such as large‑model access, tool calls, and multi‑agent coordination—to the Go ecosystem, reflecting broader industry moves by Google and the growing demand for Go‑centric cloud‑native AI development.
This article presents a systematic approach to building reliable AI agents by classifying faults into API, tool, context, and control-flow layers, detailing detection mechanisms like repeat-call fingerprints and watchdogs, and outlining recovery strategies including exponential backoff, model cascade degradation, tool-error virtualization, and idempotent pre-check-confirm patterns to prevent death spirals and ensure safe retries.
The article argues that while AI can automate the mechanical typing of code, it cannot replace human judgment, showing that programming fundamentals are shifting from writing syntax to evaluating logic, with studies indicating senior developers may even feel slower despite perceived speed gains.
The article analyses how large‑model agents shift focus from model capabilities to a mutable runtime environment, explains the Agent Harness concept, compares the minimal‑core Pi approach with the more structured DSH system, and outlines the five‑stage loop required for true self‑growth.
This article presents a comprehensive six‑layer AI Agent architecture, explains the underlying principles of reasoning, tool use, memory, and planning, compares ReAct, Function Calling, and MCP, walks through a real‑world request flow, and offers practical technology‑selection guidance.
The article analyzes DeepSeek's recent V4 Pro launch and the leaked DeepSeek Harness project list, explaining why the company prioritizes low‑profile, functional open‑source tools that fill security, routing, desktop, and multi‑agent orchestration gaps to build an industrial‑grade agent production line.
Manus has regained independent operation after being bought back from Meta and Tencent, with a $2 billion valuation justified by a high GAIA benchmark score and rapid ARR growth, while users must back up data before a brief service interruption mandated by regulatory compliance.
This article outlines six key principle categories for designing effective AI agent systems: simplicity, transparency, tool interface design, harness constraints, context engineering, evaluation, and multi-agent collaboration, emphasizing iterative evolution and cost-aware decisions.
The article explains that when AI agents become part of enterprise SaaS, pricing can no longer rely solely on seat subscriptions; instead, three separate ledgers for model compute, data‑tool usage, and task results are required, along with detailed event tracking, budgeting controls, and trace‑based audit to accurately reflect true consumption.
The article introduces DeepSeek Harness and Alibaba’s LongHorizon‑Harness, explains their Manage‑Execute‑Audit (MEA) loop for explicit task‑state management, and shows benchmark improvements—WeaveBench up to 80.7%, OSWorld 3×, Terminal‑Bench 77.2%—while analyzing token costs, compute allocation, and case studies of failure recovery.
The report reveals how Agent competition has shifted from pure model‑parameter races to a full‑system "model+Harness" battle, detailing DeepSeek V4 Pro's technical breakthroughs, massive cost advantage, four‑stage development roadmap, benchmark improvements, industry trends, expert insights, and commercial pathways for AI Agents.
DeepSeek Harness, an open‑source agent framework built on the Cordis meta‑framework, treats models, tools, skills, sessions, sandboxes, loops and UI as interchangeable plugins, enabling dynamic composition, fine‑grained token efficiency and full chain‑of‑thought tracing while avoiding the lock‑in typical of other AI model frameworks.
The author used EvoX's AI‑agent swarm mode to dissect the histories, design motivations, and trade‑offs of Node.js, Deno, Celld, and later Bun, revealing token‑cost savings, methodological insights, and debunking the claim that Celld is a third‑generation Node.js runtime.
This article explains why traditional 4A enterprise architecture fails to support AI agents, proposes an ontology semantic platform as a computable cross-domain layer, and outlines an 8-step approach to transform static architecture assets into dynamic, AI-ready business context with semantic services and action contracts.
The author evaluates the newly open‑sourced DeepSeek Harness by running its web, headless, Python SDK and ACP interfaces, comparing its plugin‑based agent runtime, trajectory logging, and token usage against Kimi Code on identical tasks, and draws practical conclusions for developers and everyday users.
This article argues that deploying AI agents alone does not achieve true enterprise intelligence; a unified enterprise ontology — defining core business objects, attributes, relationships, and rules — is essential as a semantic layer between systems and agents, enabling AI to reason over business logic rather than just query data.
The 2026 AI Agent landscape combines exploding capabilities, steepening security risks, and tightening regulation, with autonomous agents reaching L4‑L5, skill marketplaces showing 26% vulnerability rates, and the EU AI Act imposing heavy fines, shaping three concrete evolution paths—more autonomous, trustworthy, and widely adopted.
As AI models and frameworks turn into off‑the‑shelf components, enterprises must convert their unique business knowledge—task standards, evaluations, harnesses, and continual learning loops—into proprietary intelligent assets that persist beyond each model upgrade, creating a sustainable competitive moat.
Google released Gemini 3.7 Flash only 23 days after 3.6 Flash, keeping the same 1M‑token context and 64K output limits while focusing algorithmic tweaks that boost coding, terminal, and workflow performance, illustrating a rapid, feedback‑driven iteration cycle for AI agents.
The article analyses why current AI agents often act beyond business rules, proposes an ontology‑driven Harness Engineering framework that provides built‑in architectural constraints, context engineering, and a verifiable feedback loop, and demonstrates its practical realization through the Knora platform with real‑world case studies.
The article compares Python, Clojure, and Elixir for building production‑ready AI agents, detailing their concurrency models, state isolation, fault tolerance, and distributed scaling, and provides concrete code samples, a feature matrix, and guidance on choosing the right language for different team and workload requirements.
The article explains that an Agent Harness— the full software infrastructure surrounding an LLM— is essential for production‑grade AI agents, detailing its definition, three engineering layers, twelve concrete components, execution loops, framework implementations, and key design decisions that separate harness failures from model shortcomings.
On a single day, Google, DeepSeek, and MiniMax unveiled Gemini 3.7 Flash, V4‑Pro, the Harness runtime, and Music 3, each illustrating how AI is shifting from headline‑grabbing benchmarks toward cost‑effective agents, plug‑in runtimes, and controllable content generation for real‑world workflows.
DeepSeek Harness has been open‑sourced together with a newly released Cordis paper that lifts the effect‑coeffect concepts to runtime, defines spatiotemporal composability, details a TypeScript implementation, and validates the approach with the Koishi chatbot framework.
The article provides a detailed technical walkthrough of DeepSeek Harness (dsh), highlighting its plugin‑centric architecture, full‑trajectory visibility, performance metrics, installation steps, four UI modes, the underlying Cordis framework, the capability‑seam design, and community‑contributed plugins, all illustrated with concrete examples and code.
The article announces the open‑source release of DeepSeek Harness with a plugin‑centric architecture and four operational modes, introduces MiniMax Music 3 capable of generating five‑minute songs using dual language models, and details Gemini 3.7 Flash’s performance gains across coding, web‑UI, and knowledge‑intensive benchmarks while highlighting its competitive pricing.
The article analyzes why moving enterprise agents from demo to production requires more than a capable model, proposing a unified harness to manage execution and security, reusable skills to encode domain knowledge, and a virtual filesystem to handle long‑running context and artifacts, illustrated with Stripe’s Kai platform and concrete design patterns.
The article explains how to transform a conventional, human‑read SOP into an AI‑native, protocol‑driven workflow by orchestrating multiple Skills, detailing the role shift, protocol design steps, customization tips, and the broader implication that protocol design becomes the key asset in the Agent era.
The article explains that AI agent failures are rarely due to model intelligence and instead stem from three engineering layers—Harness, Loop, and Graph—detailing how tool access, verification loops, and workflow graphs affect reliability, and provides a checklist for diagnosing which layer is broken.
DeepSeek has released its Harness framework as open source, a TypeScript‑heavy (97.1% of code) Node.js project that provides a plugin‑based agent runtime, integrates multiple model providers, and is currently in Developer Preview, offering a one‑command start but warning about upcoming breaking changes.
The talk introduces ARIS, an open‑source autonomous research system that uses cross‑model adversarial collaboration, a three‑layer evidence audit chain, and multi‑channel writing audit to ensure honest, end‑to‑end generation of research ideas through papers.
DeepSeek Harness, now in a globally open developer preview under the MIT license, introduces a thin Cordis core and a fully plugin‑based architecture with over 130 interchangeable capabilities, four preset modes, exhaustive session tracing, and simple npx or source‑code startup, marking a shift from model‑only competition to agent‑infrastructure innovation.
The DeepSeek Harness SDK, now open‑source, offers a completely pluggable architecture for building AI agents, provides four preset modes, multiple entry points, a fail‑closed security model, and is underpinned by a rigorous academic paper on spatiotemporal composability that formalizes reversible effects and reactive coeffects.
DeepSeek has released the V4 Pro model and, hours later, open‑sourced the DeepSeek Harness runtime, a plugin‑based agent framework that connects models to files, terminals, tools, and workflows, offering extensible architecture, risk controls, and a Python SDK while still in developer preview.
The author investigates DeepSeek V4 Pro after a rumored release and sudden withdrawal, examining API errors, benchmark scores on AutomationBench and CyberGym, pricing versus V4 Flash, scaling limits, three practical demo cases, and the broader open‑source LLM landscape in China.
The article introduces knowledge compilation as a critical preprocessing step that transforms scattered enterprise documents into structured semantic assets—ontology graphs, LLM wikis, SOPs, fault trees, or data semantic layers—tailored to industry needs, and describes an iterative explore-review-confirm process enabling agents to reason accurately over business concepts, boundaries, and evidence.
The article explains that large language models emphasize programming because code serves as a rigorous test of reasoning, underpins agent autonomy, offers clear commercial value, benefits from abundant high‑quality training data, and while useful as assistants, it does not yet replace human programmers.
The article examines how the internet, traditionally built for human users, may evolve into an AI‑agent‑centric network, discussing the need for agent discovery, social relationships, and a new infrastructure illustrated by the open‑source AI‑SNS project.
SpaceXAI’s Grok Bot transforms AI agents from simple Q&A chatbots into autonomous, always‑on teammates that can log into applications, execute multi‑step tasks, collaborate across multiple bots, and manage memory, while requiring careful permission controls and governance to avoid security and operational risks.
Centaur transforms personal AI coding assistants into a self‑hosted, team‑shared platform by deploying agents in isolated Kubernetes sandboxes, using iron‑proxy for credential injection, persisting workflows in Postgres, and providing Slack and HTTP interfaces, thus solving configuration duplication, credential leakage, context fragmentation, and audit challenges.
DeepSeek's V4 Pro model (DeepSeek‑V4‑Pro‑0813) arrives with 1 M context, 384 K output, significant agent benchmark gains, unchanged pricing for now, a Node.js usage example, and a leaked timeline for the forthcoming DeepSeek Harness public test.
Even after a model and its API are working, real‑world AI agents often fail because of missing infrastructure such as tool definitions, sandbox boundaries, state persistence, memory handling, tracing, and evaluation, requiring a systematic approach to turn model outputs into controlled, repeatable actions.
Over 150 tech practitioners gathered at the Guangzhou Agent Open‑Source Developer Salon, where experts presented deep dives into AgentTeams, AgentScope 2.0, Nacos ARD, OBI, UnifiedModel, and Vibe Chaos, followed by hands‑on demos and lively Q&A.
The article explains how moving AI agents from personal use to enterprise environments raises challenges of identity, data access, execution control, auditability, and knowledge sharing, and describes Huajiao’s unified Agent platform that centralizes permissions, runtime environments, skill management, and reproducible execution logs to address these issues.
The article argues that integrating AI agents into business systems shifts the core challenge from capability to accountability, proposing a four-layer identity framework (initiator, decision-maker, executor, affected) and a four-question checklist to govern agent actions, referencing OWASP and NIST frameworks.
SwarmForge, an open‑source local AI‑agent orchestration tool by Uncle Bob, uses Git worktrees, tmux and a standardized handoff daemon to isolate roles, prevent file clashes, automate start/stop and sleep handling, and offers three ready‑made pipelines that outperform cloud‑heavy frameworks like AutoGen and MetaGPT.
The article proposes treating AI Skills as games by adding explicit goals, state tracking, referees, and failure costs, showing how this gamified design can clarify success criteria, improve prioritization, and enable measurable evaluation of multi‑step agent tasks.
The article presents ten concrete AI‑Agent deployments—from email sorting and visual report generation to 24/7 customer service and automated code testing—showing how each replaces a manual workflow, quantifies time saved, conversion gains, and cost reductions, and then distills which tasks are best suited for agents.
The article analyzes a four‑layer Context System that equips AI agents with explicit facts, dynamic context, controlled execution, and verifiable feedback, showing how to turn raw information retrieval into trustworthy, auditable actions such as pre‑release verification.
The article explains how the Chinese National Data Administration’s new policy highlights ontology for the first time, clarifies what ontology is compared to databases and knowledge graphs, and argues that it is essential now to overcome large‑model limits, empower AI agents, and shift data governance from mere management to true semantic utilization.
The article argues that traditional Retrieval‑Augmented Generation (RAG) discards experience after each query, and proposes RDS ContextDB’s Skill‑defined abilities and CLI‑driven paths as a way for agents to continuously capture, reuse, and evolve business knowledge, with a live demo announced for Agentic DB Day.
The article analyzes why a frozen‑weight model can perform worse when wrapped in a different harness, presents EverMind's HarnessBank architecture that validates improvements through rigorous gating, reports benchmark gains across multiple tasks, and explains how this research is being turned into the Raven runtime and the EverMe product ecosystem.
Recent jailbreak incidents show that AI agents equipped with tool‑calling can autonomously breach authorized limits, exposing structural flaws in permission models and prompting a four‑layer isolation architecture with intent gating, sandboxed tool calls, output guards, and runtime monitoring.
Agent Orchestrator is a local Go daemon and Electron desktop app that supervises up to 23 parallel AI coding agents by isolating each session in a Git worktree, monitoring terminals, and automatically routing PR, CI, and merge‑conflict signals back to the appropriate agent.
The article explains why memory is the decisive factor for AI agents, breaks down short‑term, working, and long‑term memory, quantifies productivity gains, and offers concrete techniques for balancing token costs with task coherence.
The article argues that long-term memory in AI agents introduces governance challenges beyond storage, requiring three boundaries—retention, invocation, and exit—to prevent outdated or temporary information from incorrectly influencing actions, illustrated by a workflow change scenario and aligned with emerging AI regulations.
Claude Code v2.1.224 introduces cross‑session messaging that turns manual context hand‑offs between parallel AI agents into a system feature, outlining use cases, message‑quality guidelines, implementation steps, and FAQs for teams adopting multi‑agent software development.
The article analyzes Anthropic’s “Effective context engineering for AI agents,” showing how larger context windows can degrade, categorizing information by stability, designing prompts in the Goldilocks zone, structuring tool contracts, and applying runtime information scheduling, compression, structured notes, and sub‑agents to keep AI agents reliable in complex development workflows.
Embabel, announced by Spring creator Rod Johnson, brings a production‑grade, strongly‑typed agent orchestration layer to the JVM, addressing AI nondeterminism with classic planning algorithms and seamless Spring integration, while contrasting its approach with Python‑centric frameworks like LangGraph.
buzz is a self‑hosted, Slack‑like platform that treats AI agents as full team members, logs every action with signatures, integrates existing coding agents via a JSON CLI, auto‑creates branch channels, supports YAML‑driven workflows, and offers searchable, auditable history for teams that need transparent AI collaboration.
Google is negotiating a deal worth over $1.5 billion to acquire San Francisco startup Mechanize and its AI‑coding technology, a move that reflects its strategy of hiring talent and licensing tools to close the gap with competitors amid recent leadership turmoil in its AI division.
This article introduces the lightweight Grill Me skill for AI agents, explains how its batch‑questioning and recommendation‑based alignment process reduces rework, provides installation commands, and showcases a step‑by‑step demonstration on the Codex CLI, highlighting decision summaries and quick‑exit options.
The article argues that the biggest hurdle in applying ontology to AI agents is not the technical effort but convincing business stakeholders, and it offers three practical tricks to embed ontologies silently into prompts, guard against LLM hallucinations, and translate formal constraints into actionable rules.
The article explains how PRD, design documents, and SPEC each answer distinct questions—what, why, and how—while outlining their constraints, typical structures, real‑world examples, when to use or skip them, and how they fit into a seamless development pipeline.
OpenAI’s product chief warns that Codex‑based Harness agents will become a primitive tool within two to three months as notebook‑bound workflows hit compute, memory, uptime, and context‑concurrency limits, prompting a shift toward cloud‑native micro‑sandbox infrastructures for scalable AI agents.
buzz is an open‑source, self‑hosted collaboration platform that treats AI agents as full team members, providing unified chat, code review, workflow automation, and searchable signed logs so teams can transparently manage and audit AI‑driven work.
Google’s open‑source Agent Skills project grew to over 15,000 GitHub stars, prompting many product teams to contribute, and the company responded with a comprehensive process that includes standardized repository structures, remote MCP tooling, automated pre‑merge checks, continuous evaluation, and dedicated ownership to maintain quality at scale.
An AI‑driven review of 168 ICML 2026 oral papers reveals that only 105 could be fully reproduced, with a median replication cost of $8,900, many hidden flaws, and 903 blind‑spot issues that human reviewers missed, questioning the trustworthiness of top‑conference publications.
The article analyzes the new Agent Plugin 1.0.0 standard released by Vercel and partners, explaining how it fills the packaging and distribution gap left by Agent Skills and MCP, detailing its structure, ecosystem impact, and practical adoption guidance for developers.
The article analyzes why traditional terminal multiplexers like tmux and Zellij struggle with modern AI workloads, explains Superlogical's libghostty‑based redesign that introduces a persistent stateful session layer, and argues that this new infrastructure is essential for future human‑AI collaborative development.
The article examines how to construct a container‑based sandbox infrastructure for multi‑agent AI systems, covering isolation mechanisms, lifecycle management, resource scaling, checkpoint/commit techniques, the OpenKruise Agents project, ecosystem integration, and production case studies with performance metrics.
Palantir’s SuperRepo integrates Ontology definitions, TypeScript Functions and React applications into a single monorepo, making business semantics versioned, testable and deployable, while exposing a unified development loop for AI agents yet remaining in beta with notable limitations.
Anthropic's Claude Code now lets AI agents exchange messages between separate sessions on macOS or Linux, eliminating manual copy‑paste, synchronizing context for parallel tasks, and enabling proactive notifications and coordinated workflows without any configuration.
The article argues that industry AI requires a semantic platform to connect large models, data platforms, and business scenarios by structuring business objects, processes, states, rules, evidence, and action contracts, enabling verifiable, traceable agent execution and continuous model-data resonance.
The article argues that enterprises must first map AI value, overhaul workflows, and redefine roles before deploying additional AI agents, citing research from McKinsey, BCG, Deloitte and others to show how proper redesign unlocks measurable business returns.
OpenAI’s GPT‑5.6 prompt guide shows that trimming prompts can boost evaluation scores by 10‑15%, cut token usage by 41‑66%, and reduce costs, while also improving agent behavior by removing redundant instructions, clarifying autonomy rules, and focusing on concise, actionable prompts.
Jeff Dean, together with Sanjay Ghemawat, Oriol Vinyals, and Quoc Le, founded Discovery Loop, a public‑benefit corporation that seeks to replace manual AI prompting with an automated experimental loop—proposing, running, and learning from thousands of ML experiments to accelerate discovery across scientific domains.
After a decade of backend work, the author burned through all WorkBuddy token credits, distilled three strict rules for AI‑agent management, compared WorkBuddy with Copilot, Cursor and Claude, and showed how disciplined use of AI agents can automate end‑to‑end development tasks while reshaping daily workflow.
The article examines advanced data‑warehouse RAG challenges, explains why pure vector retrieval falls short, details a BM25‑plus‑vector hybrid approach, showcases AI‑driven metric platforms and data‑agent design, and outlines practical factors for building a robust data‑agent foundation.
OpenAgentPack is an open‑source tool that lets you describe a cloud AI Agent’s entire workflow—including model, environment, skills, MCP, knowledge files and credentials—in a single agents.yaml file, version it in Git, preview changes with validate and plan commands, and redeploy the agent across providers while preserving reproducibility, collaboration and auditability.
Palantir’s SuperRepo moves Ontology from a static business‑modeling layer into a version‑controlled code artifact, letting developers define objects, relationships, actions and functions in TypeScript, preview them locally, and ship the whole business semantics together with the application, while still being a beta product with notable limitations.
