JetBrains GameDev Day is back! Join us online on September 28, 2026, for a day dedicated to game development, practical engineering, tools, workflows, and the people building games across the world.
We’re looking for speakers who are excited to share their knowledge, experience, lessons learned, tools, or creative approaches with the gamedev community.
GameDev Day is a free, live, online event hosted by JetBrains and shaped by the community.
It brings together developers, tech artists, toolsmiths, engine programmers, and teams working across different parts of game development, including engine internals, CI/CD pipelines, debugging, performance, architecture, developer tooling, production workflows, AI, and more.
Whether you’re working with Unity, Unreal Engine, Godot, custom engines, internal tools, or production pipelines, we’d love to hear from you.
What kind of talks are we looking for?
We welcome talks that are practical, thoughtful, technical, or experience-driven – anything that can help, inspire, or challenge fellow game developers.
Topics may include, but are not limited to:
Engine-focused workflows and optimizations: Unity, Unreal Engine, Godot, custom engines.
Performance optimization, memory management, profiling, and debugging workflows.
Game architecture: balancing maintainability, performance, and production realities.
CI/CD pipelines, build automation, testing, and deployment workflows.
AI-assisted and agentic development workflows in gamedev.
Developer tools, internal tools, editor extensions, and open-source libraries.
Cloud-based workflows and distributed development for game teams.
C++, C#, Kotlin, and other languages used in game development.
Cross-platform development and build complexity.
Collaboration between engineers, tech artists, designers, and production teams.
Lessons learned from shipping, scaling, or maintaining games and tools.
We’re especially happy to see talks that showcase how JetBrains tools support your work, but this is completely optional. If you have a story, idea, tool, workflow, or experience that others can learn from, we’d love to hear about it.
Talk format
All talks will be presented live and in English.
Suggested session formats:
30-minute talk
45-minute deep dive
60-minute technical session or demo-driven talk
Each session may include a short Q&A. We’ll work with selected speakers to find a time slot that fits their time zone and schedule, and we’ll support speakers with preparation, dry runs, and feedback if needed.
Every session will be streamed live and recorded, with videos published on the JetBrains YouTube channel and promoted across our newsletters, blog, and social media.
As a speaker, you’ll also receive a complimentary one-year JetBrains All Products Pack subscription and promotion of your personal blog, project, course, or community initiative, if you’d like to share one.
Watch parties
This year, we’re keeping the same practical, community-driven spirit while making the event even more connected. GameDev Day 2026 will be a one-day online experience with live sessions, discussions, and community watch parties.
One of the planned locations for GameDev Day’s offline presence is the JetBrains office in Limassol, where we’ll host our own local watch party for the community.
This year, we’re also planning a special offline watch party in collaboration with DevGAMM FTW! Belgrade, where GameDev Day will be streamed as part of a pre-event community activity before the main DevGAMM conference. This means speakers and attendees will have a chance to reach both the global online audience and a highly relevant in-person gamedev audience in Belgrade.
More details about watch party locations and registration will be shared soon.
Ready to submit?
We’d love to hear what you’ve been working on, struggling with, building, improving, or thinking deeply about. If you have something to share with the gamedev community, send us your proposal.
The Call for Speakers will remain open until August 12, 2026.
Outer Solar System Entering the area of gas and ice giants What follows is part 3 of my ongoing observations and ideas inspired by “Project Hail Mary”. You can find part 1...
Many companies silently assume that everybody wants more AI in their lives. That people are craving new AI features, new AI products, new AI workflows — that would all magically replace all existing outdated practices and broken ways of working.
But in reality, it seems like people don’t want more AI at all — at least not in the way most AI leaders envision it. Unsurprisingly, many AI features have low adoption and retention — at a very high cost of delivery, and a high risk of reputation damage.
The AI People Don’t Need
It’s remarkably difficult to make a strong argument with senior leadership, but AI is not a value proposition. New AI features don’t magically make for happy or excited customers. Because AI features are often bolt-ons and separate tools for employees to use, they typically take people out of their regular way of working.
AI is pretty good at amplifying shortcuts and shortcomings in organizations — from data quality to decision making. It can’t magically fix years of accumulated quick patches, technical debt, broken culture and internal politics. If anything, they become more visible with AI as inconsistencies or conflicting priorities and get handed directly to users, who are then left to make sense of the mess themselves.
Because in most organizations, work typically requires hopping on and off between plenty of disconnected and fragmented systems, with a new AI tool, they now have yet another system that they also need to hop on and off. Often it produces more work, and typically it’s not particularly rewarding work either.
On top of that, people are very much aware of the cost of finding and fixing AI hallucinations. Asking AI to generate a response might feel easier than writing from scratch, but it has a cost:
Skim through the entire AI output,
Spot key points to focus attention on,
Review/verify key points, one-by-one,
Check rationale for what follows next,
Articulate corrections + regenerate,
Review the response (a number of times).
For many people, AI isn’t something they can proactively choose and explore on their own — it arrives uninvited, at someone else’s pace. On top of that, plenty of messages amplify fears and worries about AI replacing work — so it’s hardly surprising that the perception of AI isn’t excitement. It’s resistance to change and deep anxiety about one’s place in a world that seems to be changing without them.
At best, AI features might be silently accepted or nodded away. At worst, AI raises concerns, doubts, caution — and calls for a healthy dose of skepticism. And sometimes it’s perceived as a threat or liability — because unlike other features, AI is neither predictable nor reliable.
People don’t dream of AI art museums or AI fridges or AI hotel reception or AI-narrated children’s books. They don’t want their children to have romantic AI partners. Most people don’t want to actively manage (and clean up after) a swarm of AI agents roaming in their bank accounts and acting on their behalf in the real world. And most notably, people don’t really want a magical box to speak to or type into all the time.
The AI People Actually Need
I’m always puzzled by the comparison of AI features with how unreliable humans are. But people don’t compare software with other people. They compare features with features — and if one feature in one product is unreliable, while a similar feature works flawlessly in another, they choose the latter. It’s not about AI or not AI, but rather what works consistently and reliably, and what doesn’t.
Many conversations about AI are conversations about the speed of delivery. But to many people, there is little value in increasing the speed of delivery. They want to do things well, with enough time to think and make good decisions. They also want to enjoy the time they spend working on things, rather than just ship faster. There is an enormous feeling of reward and achievement that slowly disappears, one vibe-coded change at a time.
People don’t change much. And after all these years, they (still) want features that are fast, accessible, reliable, predictable and useful — every single time. And ideally not the ones that replace their entire workflow, but that augment their way of working — and that take over the most mundane, annoying, and boring tasks that they find no pleasure in.
Many jobs are exposed to AI automation, but in many of them there is a rewarding, unique, creative part that requires taste, point of view, and perhaps even human intuition. And if AI automates boring parts of it, that’s an advantage for everyone. That’s also what enhances productivity and brings more joy in daily life.
When AI automates tedious and mentally exhausting tasks, its value is much easier to grasp. But for that, AI shouldn’t feel like a bolt-on. It should be deeply integrated into people’s existing workflows. It must also match existing mental models that they have developed and fine-tuned for years or decades. AI should adapt to how people think and make decisions, not the other way around.
And it doesn’t really matter if these features are branded as “AI”, “smart” or “automation”. However, they must work well for people using them. And that means that people must be aware of use cases where it actually helps them, and be inspired to find more use cases on their own.
Ironically, tools that work well there aren’t “AI-first” — they are “AI-second”. Subtle, humble, calm, ambient, taking a supportive role in the background for work that otherwise is remarkably dull and unnecessary.
I don’t want to read books written by AI. I don’t want to gaze upon paintings by AI. I don’t want AI to teach my children. I don’t want to have an AI therapist. I don’t want AI making my medical decisions. I want AI to do all the physical and mental labor that taxes me so I can read books written by humans and go to art galleries to engage with art made by humans. I want AI that makes my life easier rather than forces me to change myself.
Perhaps I’m missing a bigger picture, and perhaps I’m just old school — but I really do like people. Their stories, their thinking, their emotions, their enthusiasm, their laughing. AI can be remarkably helpful in many situations, but so are people. And between the two, I would favor spending time with a human — however imperfect they are — every single time.
No, people don’t need more AI in their lives — they need AI to automate all the boring stuff they have to deal with every day, so they have more time and headspace to do things that they actually love and enjoy doing. That doesn’t mean spending more time with AI — but spending more time with people they love.
The following article originally appeared on Paolo Perrone’s Substack, The AI Engineer, and is being republished here with the author’s permission.
You spent three weeks shipping an agent. It worked in the demo. Then production hit, and you realized the framework you picked has no checkpointing, the memory layer is a flat vector dump with no temporal reasoning, the browser tool falls over on any site with a canvas element, and the eval suite is a Notion doc someone keeps forgetting to update.
The open source toolkit for building agents in 2026 has solved most of these problems. The catch is that it has solved each one in a dozen incompatible ways. The memory framework that wins LoCoMo (the standard long-conversation memory benchmark) runs 340x heavier per conversation than the runner-up, a difference no benchmark column shows. The same gap between benchmark score and production behavior shows up at every layer.
So the best way to zero in on the constraint your system will hit first under load: latency budget, audit trail, model portability, or language stack. Get this wrong and you rewrite your state schemas in week three.
TL;DR
If you read “The AI Agents Stack (2026 Edition),” this is the open source half. Same seven layers around the think-act-observe loop from “What Is an AI Agent?”: orchestration, memory, tool interface, browser/CUA, coding agents, evals and observability, and inference. Here’s where to start at each layer.
How to pick at each layer
When choosing tools at each layer, ask three questions:
What’s the dominant constraint? Four constraints decide most layer picks. Latency budget is how many tokens or milliseconds you can spend per turn. Audit trail is whether every action has to be traceable for compliance. Model portability is how tied your stack gets to one provider. Language stack is whether your team is Python, TypeScript, or both. One of these usually dominates at each layer.
What’s the rip-out cost if you’re wrong? Swapping an MCP server changes one config line. Swapping orchestration rewrites your state schemas, your nodes, and your edges. The bigger the rewrite, the more you should pick by constraint first.
Is it open source or open core? Open core means the project ships under an open source license, but production features (multitenant auth, replication, SSO, audit logs) only run in the managed cloud product. The repo’s feature list tells you which side of the line you’re buying.
Layer 1: Orchestration and runtime control
The orchestration layer runs the agent’s reasoning cycle. The LLM picks an action, the runtime executes it, the runtime observes the result, and the LLM picks again. If you skip a framework here, you write the loop yourself, which means reinventing retries, checkpointing, and human-in-the-loop gating before you ship.
LangGraph is the default for Python production work. Graph-based state machine, durable execution via PostgresSaver, time-travel debugging, and the largest verified enterprise list in the field (Klarna, Uber, LinkedIn, JPMorgan, Replit). Graph state maps onto what regulated industries need: Every state transition is an audit log entry, and any failed run rolls back to a prior node and replays from there. The ceiling: It’s verbose. A two-agent flow still needs a state schema, nodes, edges, and compilation. For “call three tools sequentially,” it’s overkill.
CrewAI has the lowest setup overhead of the four orchestration frameworks. You declare roles like researcher, writer, and reviewer, pick a coordination pattern, and run the crew with no state schema to define first. The ceiling: CrewAI optimizes for prototype velocity at the cost of production durability. The framework can’t resume crashed runs from where they failed, error handling lives at the crew level rather than per-node, and no inspectable state schema records what the agents decided and when. Teams move from CrewAI to LangGraph when production state management starts mattering more than the role metaphor.
Pydantic AI treats every agent output as a typed Pydantic model, so validation, retries, and downstream serialization come for free. FastAPI-style decorators for tools and dependencies. The ceiling: Pydantic has weaker multi-agent primitives than CrewAI or LangGraph. It’s the best fit when the agent is a single loop that has to return validated data to a downstream service.
Mastra is the TypeScript answer: agents, workflows, RAG, and evals in one package, built by the ex-Gatsby founders, designed to drop into existing Next.js apps without a Python sidecar. The ceiling: smaller ecosystem and fewer production case studies than LangGraph. Choose Mastra when the team is already on TypeScript end to end and rewriting in Python isn’t on the table.1
The vendor SDKs (Claude Agent SDK, OpenAI Agents SDK, Google ADK) belong here too. Each one removes orchestration friction and locks the agent to one provider’s API. Pick one if you’re already committed to that provider and not planning to swap models.
Layer 2: Memory and state
The context window isn’t memory. Even at 200K tokens, every turn pays for the entire conversation again, and nothing survives the session. Production agents in 2026 keep memory in a dedicated layer that lives outside the prompt.2
Mem0 memory can be scoped to a user (persists across all their sessions), a session (just this conversation), or an agent (shared across all users of one agent). Hybrid storage combines vectors and a graph, with mature SDKs that plug into LangGraph, CrewAI, and Mastra. The project has 48,000+ GitHub stars. Mem0’s ECAI 2025 paper benchmarked Mem0 against 10 alternatives on LoCoMo and reported 92% lower latency and 93% fewer tokens versus naive full-context (the baseline every team replaces by week two), which translates to roughly 14x cheaper inference at the same recall.3 The ceiling: Mem0 treats memory as retrieval, returning the most similar facts to a query. Temporal reasoning, like “what did the user say last week that contradicts what they said today,” needs a graph that tracks edges between facts with timestamps.4
Zep/Graphiti is the temporal graph option. The knowledge graph layer handles entity resolution: figuring out that “Alice,” “alice@acme.com,” and “the CEO” all refer to the same person. It also tracks how relationships change over time, so the agent can answer, “What did this customer’s status look like in Q2?” or “When did the contract owner switch?” The trade-off is that graph construction is expensive. Zep’s memory footprint per conversation runs past 600,000 tokens versus Mem0’s 1,764, and immediate postingestion retrieval often fails because correct answers only appear after background graph processing completes. Choose Zep when the agent needs to reason about history and you can wait seconds, not milliseconds, between turns.
Letta (formerly MemGPT) treats memory like an operating system. Main context is RAM, archival memory is disk, and the agent decides what to promote into RAM, archive to disk, or forget. It’s fully open source, model agnostic, and self-hosted from day one. The architecture extends an agent’s effective context far beyond the LLM’s native window by paging memory in and out, the same trick operating systems use to give programs more virtual memory than physical RAM. The ceiling: You run the storage layer yourself. Letta is harder to deploy than calling a hosted Mem0 endpoint and harder to debug because memory decisions happen inside the agent at runtime.5
Engineering lesson. “Memory” means two different things in an agent system, and using one tool for both breaks both. Runtime state is the agent’s scratchpad mid-task: which node it’s at, what tools it called, what intermediate results it has. LangGraph’s PostgresSaver writes this after every step, so a crashed run resumes from the last node. Knowledge memory is what the agent learned across sessions: preferences, prior questions, and facts about the user. Mem0 and Zep store this. Conflate them and you get an agent that resumes a crashed run correctly but forgets the user the moment they open a new session, or one that remembers the user but can’t recover when it crashes mid-task.
Layer 3: Protocols and tools
Two years ago this layer was function calling: Each provider had its own JSON schema, and each framework wrapped them differently; switching models meant rewriting your tools.
In 2026 this layer is MCP. The Model Context Protocol is the open standard the Claude Agent SDK uses, that OpenAI Agents SDK supports natively, that Google ADK integrates with, that every serious framework now ships a client for. If you’re writing tools today, you’re writing MCP servers. If MCP itself is fuzzy, “What Is MCP?” is the prerequisite.
There’s no framework to pick at this layer. The orchestration choice from layer 1 already decided how MCP integrates.
FastMCP is the Python framework for writing MCP servers fast. Decorator-based and async-first, it’s the closest thing to FastAPI for MCP. mcp-agent is an orchestration framework built around MCP as the primary tool interface. Server lifecycle, multiserver routing, and prompt context handling are built in. With LangGraph or CrewAI, you write that integration code yourself. It’s worth looking at when your agent connects to several MCP servers and the integration code starts becoming the bottleneck.
Layer 4: Browsers and computer use
When the system the agent has to act on doesn’t expose an API, the toolkit has to act through screens. The 2026 field split into two architectural approaches: DOM-driven (parse the page, find elements, and click them) and vision-driven (screenshot the page, feed it to a vision model, and click pixels).
Browser Use is the Python default. With 50,000+ GitHub stars, it’s one of the fastest-growing open source AI projects of 2025–2026. The LLM gets full control of the browser through an agent loop and integrates with LangChain, CrewAI, and custom frameworks. The ceiling: Every step costs an LLM call, which is fine for novel tasks and brutal for repeated workflows. Production teams cache the repeated 80% in Playwright (the deterministic browser automation library) and leave Browser Use for the 20% that needs reasoning.
Stagehand is the TypeScript answer. It’s an open source, MIT-licensed SDK from Browserbase, built as a layer on top of Playwright. Four primitives let the developer keep AI inference for the steps that need reasoning and use scripted Playwright code for the rest. Stagehand v3 (February 2026) rewrote the engine on top of Chrome DevTools Protocol and ships 44% faster.6 The ceiling: Production deployment runs through Browserbase’s managed cloud. The open source SDK is the on-ramp.7
Skyvern is the vision-first option. Each task runs through a three-phase pipeline: Planner breaks the goal into steps, actor sends a screenshot to a vision model and clicks the coordinates it returns, and validator confirms the page changed. Skyvern scores 85.85% on WebVoyager 2.0, the strongest published score on form-filling tasks in domains where the DOM is unreliable: canvas elements, React virtual DOMs nested in iframes, or antibot machinery. That score still translates to roughly one in seven multistep tasks failing. The ceiling: Vision-driven stacks lag DOM-driven ones by 12–17 points on common tasks and cost 4–8 times more per step.8
The production pattern in 2026 wires both in: DOM-driven as the primary path, Skyvern or Anthropic Computer Use or OpenAI CUA as the escape hatch when selectors keep failing on canvas elements or antibot screens. Edge surfaces are one of the four agent failure modes, and we cover all four in “Why AI Agents Keep Failing in Production.”
Layer 5: Coding agents and sandboxes
Coding agents are a category of their own now. They write code, run it, debug it when it breaks, and read docs to figure out what they got wrong. This layer ships with three things the other six don’t: a sandboxed filesystem to write and edit code without escaping into the host, terminal access to run builds, tests, and linters, and a browser tool because half the work involves reading docs. The category also has its own benchmark, SWE-bench Verified, a curated set of real GitHub issues an agent must resolve into a working PR. For the closed-source comparison, see “Cursor vs Claude Code.”
OpenHands (formerly OpenDevin) is the production-grade autonomous option. It has 72,000+ GitHub stars, completed a $18.8M Series A, and is used in production at AMD, Apple, Google, Amazon, Netflix, and NVIDIA. The event-stream architecture moves through four states per loop: Agent reasons, agent emits an action, environment executes it, environment returns an observation. Each session runs in an isolated Docker sandbox. The benchmark question for this category is what percentage of real-world bug tickets the agent can resolve end to end without human input. OpenHands scores 53%+ on SWE-bench Verified with Claude 4.5 and up to 72% with Claude 4 on the published platform results. The ceiling: The agent has shell access. Review can’t live inside OpenHands; it has to live at the PR.9
Aider is the terminal-native option. The original open source coding agent, it has 35,000+ GitHub stars and 13,100+ commits across 93 releases. It’s Git-integrated by design: Every change becomes a commit with an auto-generated message that names what it touched, so the entire agent session is in your git history. Architect/Editor mode splits the work between two models: A stronger one plans the edit, while a cheaper one writes the code. The split cuts cost 30%–40% versus running a top-tier model on every token. Aider scores 32% on SWE-bench Verified with Claude 4.5, well below OpenHands, but it ships fewer surprises because every action lands in Git. The ceiling: It’s terminal-only. There’s no IDE integration and no project-wide context beyond what Aider parses from the files you pass it.
Cline is the VS Code-native answer. It’s fully open source and modelagnostic, with 38,000+ GitHub stars, and it’s the only option here with a meaningful market share inside VS Code teams. Plan Mode and Act Mode separate intent from execution: Plan Mode drafts the change list and pauses for approval, and Act Mode executes the approved plan. Every action is reviewable before it touches the codebase, which is the design point engineering managers ask about first. Choose Cline when the team lives in VS Code and human review on each step is required by policy. The ceiling: It’s IDE-locked. JetBrains or Neovim teams should look at Continue or the terminal tools above.
Most teams running production coding agents in 2026 run two: one commercial (Claude Code, Codex) for hard tasks and one open source for flexibility and outages. “How Cursor Actually Works” shows what the leading commercial coding agent actually does under the hood.
Layer 6: Evals and observability
The evals and observability layer records what the agent did in production and tests what it can do before shipping. Tracing captures every LLM call, tool invocation, and cost, indexed by user and session, so when an output is wrong, you can replay the exact context that produced it. Evals are reproducible test suites the agent runs against fixed inputs with pass/fail criteria scored the same way every time. Production-grade agent teams in 2026 wire both in on day one. Skipping this layer is the most expensive mistake in agent engineering.
Langfuse is the open source observability default. It’s open core with a generous self-hosted tier and native integrations with LangGraph, CrewAI, OpenAI Agents SDK, and Mastra. Every LLM call, tool invocation, and cost gets traced and indexed. The ceiling: Managed retention, SSO, and advanced eval features run on the SaaS plan. The self-hosted version covers tracing and dashboards.
Arize Phoenix is the OpenTelemetry-native alternative. Traces flow into the same Grafana, Datadog, or Honeycomb dashboards the rest of your stack already uses, so agent telemetry sits next to your API and service traces instead of in a separate tool. It’s strong on RAG evals and retrieval quality. The ceiling: Phoenix doesn’t ship opinionated agent-specific defaults. The pipeline assembly is on you.
Inspect AI is the UK AI Security Institute’s open source eval framework. The institute wrote it for safety evals: testing whether the agent refuses jailbreaks, leaks PII, or generates unsafe content. Frontier labs now use it for capability and alignment benchmarking too. The ceiling: Inspect is for offline evaluation. If you also need to see what the agent is doing live in production, you’ll want Langfuse or Phoenix next to it.
Engineering lesson. Wire tracing in on Day 1, before the first user. Setting up Langfuse or Phoenix at project start is a couple of hours of config work. Without those records, debugging a production failure means guessing which prompt version, which user input, and which tool sequence produced it.
Layer 7: Models and inference
Every step an agent takes is at least one inference call, often more. The engine running those calls, the software wrapping the GPU, batching requests, and managing the KV cache, sets the cost floor for everything else. Hosted API agents inherit their provider’s engine. Self-hosted agents pick their own, and the pick determines what the agent costs to run at scale.
vLLM is the production serving default for open-weight models. Its core innovation is PagedAttention, a memory management trick that splits the KV cache into fixed-size blocks so multiple requests share GPU memory without wasted space. Combined with continuous batching, it produces the highest throughput-per-dollar in the field. The ceiling: vLLM is GPU only and optimization heavy, and it assumes the operator knows what KV cache means.
Ollama is the local default. After a one-line install, it downloads quantized models from a registry and exposes an OpenAI-compatible API. Quantization compresses weights from 16 bits down to 4 or 8, trading a small accuracy hit for fitting in laptop RAM. The ceiling: Ollama isn’t a production serving layer past a single user.
llama.cpp is the engine Ollama runs on top of. Pure C++ with no GPU dependency, it runs LLMs on CPU, Apple Silicon, Raspberry Pi, and anything else with enough RAM. The project also defined GGUF, the file format used to ship quantized open-weight models, so the same model file runs across every llama.cpp-based tool unchanged. The ceiling: CPU throughput sits well below GPU serving, which makes llama.cpp the right pick for local and offline workloads only.
SGLang is the newer challenger. Two design choices set it apart. First, when many requests share an opening prompt, SGLang caches the computation of that prefix once and reuses it, instead of recomputing it for every call. Second, when the agent needs JSON output, SGLang enforces the schema inside the inference engine itself, so the model can’t generate invalid JSON in the first place. On agent workloads, SGLang benchmarks faster than vLLM. The ceiling: There’s a smaller community and fewer integrations, and it’s less battle-tested than vLLM in production at scale.
The instinct when reading a seven-layer diagram is to assume the layers compose vertically: Pick layer 1, that constrains layer 2, which constrains layer 3, and the right toolkit is the one where every box fits together.
Most agent rewrites in 2026 trace back to a team that built on that assumption. No ecosystem is best in class at all seven layers, and the integrations between layers were never designed to compose. They meet at thin seams: a config file, an import, an HTTP call. . .
The seven layers are seven independent decisions. Each one has a dominant constraint that picks the winner. Four constraints decide most picks: latency budget, audit trail, model portability, and language stack.
The four constraints rarely point at the same winner. Latency-first stacks pull toward Mem0 and vLLM. Audit-first stacks pull toward LangGraph and Langfuse. Model portability pulls away from vendor SDKs. Language stack pulls toward Mastra or Pydantic AI. Trying to satisfy all four with one ecosystem means picking the average tool at every layer instead of the best one at each.
The reframe: An agent’s toolkit is seven small bets, each with a single dominant constraint, and each made independently. The teams shipping reliable agents in 2026 are the ones who picked the best tool per layer and accepted that integrating the seams is part of the job.
The agent stack cheat sheet
Before swapping any layer in a production agent, check this table first. The state column tells you how much you have to migrate. The lock-in column tells you what you’re giving up if you switch. The demo-to-prod column tells you how long the swap will actually take.
The House voted 308-117 to pass the Sunshine Protection Act, which would make daylight saving time permanent nationwide and end the twice-yearly clock change. The bill faces an uncertain future in the Senate, "where one G.O.P. leader said it was unclear whether it could move ahead and at least one Republican appears inclined to try to block it," reports The New York Times. Some sleep experts oppose permanent daylight saving time, arguing that year-round standard time better aligns with circadian rhythms and winter morning safety. The New York Times reports: President Trump has championed the effort to save an extra hour of daylight before nightfall and make the time zone permanent, describing the ritual of moving clocks forward in the spring and back in the fall a "ridiculous, twice yearly production." "We are going with the far more popular alternative, Saving Daylight, which gives you a longer, brighter Day," Mr. Trump wrote in a social media post in May. "And who can be against that."
A sizable bloc of Florida Republicans in Congress is leading the charge on legislation that would do just that, mandating daylight saving time nationwide for the entire year. Representative Vern Buchanan of the Tampa Bay area is backing the bill, and Representative Anna Paulina Luna, another Tampa Bay-area Republican, cosponsored it. House leaders agreed to allow a vote on the measure this week as a sweetener for Ms. Luna in their efforts to persuade her to lift a legislative blockade she had maintained as she sought to force Senate action on a voting restriction bill Mr. Trump has championed.
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