Originally published at twarx.com - read the full interactive version there.
Last Updated: June 25, 2026
The Interactions API Gemini models agents endpoint just made every LangGraph pipeline, every AutoGen workflow, and every hand-rolled agent memory system you built in the last two years a liability — Google turned stateful agent orchestration into a managed cloud primitive on June 23, 2026.
The Interactions API reached general availability as Google's primary interface for Gemini models and agents — a single unified endpoint with server-side state, background execution, tool combination, and Managed Agents. It is not an upgrade to the Gemini API; it is a deliberate architectural displacement of the third-party orchestration ecosystem.
By the end of this article you will know exactly what the Interactions API does, how to call it, what it costs, when to migrate, and whether your existing stack just became dead weight. For the broader context, see our overview of AI agent orchestration and our running Gemini API changelog.
Google's official announcement of the Interactions API reaching general availability — a single unified endpoint for Gemini models and agents. Source
Coined Framework
The Stateful Sovereignty Layer
The emerging architectural tier where cloud providers absorb all agent memory, tool routing, and execution state that developers previously had to build and maintain themselves. It names the systemic shift that makes traditional orchestration frameworks structurally redundant — not by outcompeting them on features, but by absorbing the very problem they were created to solve.
What Google Announced: Interactions API Reaches General Availability
The Official Announcement: Date, Source, and Exact Claims
On June 23, 2026, Google announced via The Keyword (blog.google) that the Interactions API has reached general availability and is now its primary API for interacting with Gemini models and agents. The post was authored by Ali Çevik, Group Product Manager at Google DeepMind, and Philipp Schmid, Developer Relations Engineer at Google DeepMind.
Per the announcement: "Today we're announcing that the Interactions API has reached general availability and is now our primary API for interacting with Gemini models and agents." Google confirms it "launched its public beta in December 2025" and that it "has quickly become developers' favorite way to build applications with Gemini."
Why June 23, 2026 Is a Pivotal Date for AI Development
This is the date Google stopped treating agentic orchestration as a developer's problem and started treating it as cloud infrastructure. The announcement explicitly states: "All of our documentation now defaults to Interactions API and we are working with ecosystem partners to make it the default interface across 3P SDKs and Libraries." That single sentence reframes the entire agent orchestration landscape.
What Changed from Preview to GA: Stable Schema and New Features
The GA release brings a stable schema — a direct answer to developer complaints about breaking changes during the December 2025 beta. Google adds "major new capabilities that developers asked for, including Managed Agents, background execution, Gemini Omni (soon) and more." The standout: Managed Agents, where "a single API call provisions a remote Linux sandbox where an agent can reason, execute code, browse the web and manage files," with the Antigravity agent shipping as the default.
Dec 2025
Public beta launch of the Interactions API
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
1
Unified endpoint for both model calls and agent runs
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
1 call
Provisions a remote Linux sandbox for a Managed Agent
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
Google didn't release a better orchestration framework. It made orchestration frameworks a category you no longer need to choose between.
What Is the Interactions API? A Technical Definition
From Stateless Text Generation to Stateful Autonomous Workflows
The Interactions API is a single unified endpoint that handles both single-turn model calls and multi-turn agentic workflows under one surface. As the announcement puts it: "Whether you're calling a model or running an agent, the Interactions API gets you there in a few lines of code. Pass a model ID for inference, an agent ID for autonomous tasks, set background=True for anything long-running."
The legacy Generate Content endpoint was stateless — every call carried the entire conversation history, every tool result had to be re-sent, and developers built session management, memory stores, and retrieval loops themselves. The Interactions API maintains server-side state: conversation history, tool call results, and agent memory live in Google's infrastructure, not your database. We unpack the architecture further in our guide to stateful agents.
The Stateful Sovereignty Layer: Why This Architecture Shift Matters
This is the architecture I call the Stateful Sovereignty Layer. When the cloud provider owns state and execution, the middleware that existed purely to assemble those pieces loses its reason to exist. It's the same dynamic that hollowed out third-party CI/CD tooling once GitHub Actions shipped natively.
Coined Framework
The Stateful Sovereignty Layer in practice
Your agent's memory, tool routing, and execution state stop living in your codebase and start living in Google's. The orchestration logic you wrote to glue stateless calls together becomes a maintenance burden rather than a moat.
How the Interactions API Differs from the Legacy Generate Content Endpoint
Three structural differences: (1) it was designed for agentic workflows as its primary goal, not retrofitted like the OpenAI Assistants API; (2) it supports background execution so long-running tasks don't require an open HTTP connection; (3) it eliminates the need to manually implement RAG retrieval loops, tool result caching, and session management. You bring intent; Google brings the plumbing.
The most underrated line in the announcement is "set background=True for anything long-running." That single boolean kills the entire category of webhook-juggling glue code that orchestration frameworks were built to manage.
The shift from stateless text generation to the Stateful Sovereignty Layer: Google now owns the memory and execution tier that developers previously assembled by hand.
Full Capability Breakdown: What the Interactions API Can Do
Server-Side State Management: Memory Without a Database
Server-side state persists conversation history and tool results inside Google's infrastructure. For many workflows this removes the need to stand up a vector database like Pinecone or Weaviate purely for session memory. Note the nuance: this is conversational and execution state, not a replacement for retrieval over a large private corpus — you'll still want a vector store for genuine knowledge retrieval, as we explain in our vector databases primer.
Background Execution: Long-Running Agents Without Open Connections
Setting background=True tells the server to run the interaction asynchronously. This is the answer to the 30-second HTTP timeout wall that breaks long agent runs. The announcement is explicit: "Set background=True on any call. The server runs the interaction asynchronously."
Tool Combination and Multimodal Input Support
The GA release brings "Tool improvements: Mix built-in tool[s]" — combining Google-native tools like Search and Code Execution with custom MCP-compatible tools in a single interaction. Multimodal support spans text, image, audio, and video within one stateful session.
Managed Agents: Cloud-Sandboxed Autonomous Execution
This is the headline. "A single API call provisions a remote Linux sandbox where an agent can reason, execute code, browse the web and manage files. The Antigravity agent ships as the default, and you can define your own custom agents with instructions, skills and data sources." Think of it as AWS Lambda for agent execution — isolated, ephemeral, and infrastructure-free.
New Developer-Requested Parameters
The GA features were "capabilities that developers asked for." Combined with Gemini's reasoning controls, you get explicit knobs for how much compute the model allocates to a task — directly impacting latency and cost per call. Gemini Flash models remain the cost-optimized choice for high-volume agentic loops.
How a Managed Agent Request Flows Through the Interactions API
1
**Client sends one call (agent ID + background=True)**
No session table, no memory store, no webhook scaffolding. The developer passes intent and an optional webhook URL for completion.
↓
2
**Interactions API provisions a Linux sandbox**
A single call spins up an isolated remote environment running the Antigravity agent (or your custom agent) with its skills and data sources.
↓
3
**Agent reasons, executes code, browses, manages files**
Server-side state captures every tool result and reasoning step. No open HTTP connection is required because execution runs asynchronously.
↓
4
**Completion callback fires to your webhook**
The result returns with full state preserved server-side, retrievable by session ID for the next turn.
The sequence matters because every step that used to be developer-owned infrastructure is now absorbed into a single API surface.
A single API call now provisions a Linux sandbox that can reason, run code, and browse the web. The infrastructure layer of agent development just disappeared into a function argument.
How to Access and Use the Interactions API: Step-by-Step Guide
Prerequisites: API Key, SDK Version, and Account Requirements
You need a Google AI Studio API key or Vertex AI credentials — both are supported at GA. All official documentation now defaults to the Interactions API, so the SDK examples you'll find lead with it rather than Generate Content.
Making Your First Interactions API Call
Python
Single-turn model inference — pass a model ID
from google import genai
client = genai.Client(api_key='YOUR_API_KEY')
The simplest possible call: one model ID, one prompt
response = client.interactions.create(
model='gemini-flash', # cost-optimized for high volume
input='Summarize Q2 sales trends in 3 bullets'
)
print(response.output)
Implementing Stateful Multi-Turn Conversations
Python
Server-side state: pass a session_id, Google persists history
first = client.interactions.create(
model='gemini-flash',
input='My company sells artisan coffee subscriptions.',
session_id='customer-42' # state now lives in Google infra
)
No need to resend history — the session remembers
second = client.interactions.create(
model='gemini-flash',
input='Draft a win-back email for churned subscribers.',
session_id='customer-42' # context carried automatically
)
print(second.output)
Adding Tools and Enabling Background Execution
Python
Run a Managed Agent in the background with mixed tools
job = client.interactions.create(
agent='antigravity', # default Managed Agent
input='Research competitor pricing and write a CSV report.',
tools=['search', 'code_execution'], # built-in tools combined
background=True, # async — no open connection
webhook_url='https://yourapp.com/agent-done'
)
print('Job started:', job.id) # poll or wait for the callback
For pre-built agent patterns you can adapt rather than write from scratch, explore our AI agent library — most translate cleanly onto the Managed Agents model. If you're starting fresh, our ready-to-deploy agent templates map directly onto the Interactions API surface.
Pricing, Rate Limits, and Availability by Region
Pricing follows the established per-token Gemini model — there is no separate orchestration fee, which is the structural advantage over hosted framework offerings that bill execution separately. Gemini Flash remains the cost-optimized tier for high-volume agentic calls. For exact current rates, consult the official Gemini API pricing page, as token rates vary by model and change over time. The GA documentation now defaults to the Interactions API across all SDKs, and Google is working with ecosystem partners to make it the default interface across third-party libraries. See our Gemini API pricing guide for worked cost examples.
The pricing detail that matters most: Interactions API bundles orchestration and execution into the per-token cost. LangGraph Cloud and similar hosted runtimes bill execution as a separate line item — meaning Google can undercut on total cost of ownership without touching the model price.
A stateful multi-turn agent in a handful of lines: the session_id and background=True parameters replace what used to be hundreds of lines of orchestration glue.
[
▶
Watch on YouTube
Google DeepMind walkthroughs of the Gemini Interactions API and Managed Agents
Google DeepMind • Gemini agent architecture
](https://www.youtube.com/results?search_query=google+gemini+interactions+api+agents)
When to Use the Interactions API vs. Alternatives
Use Interactions API When: Ideal Scenarios and Workloads
Reach for it when you're running long agents (over the 30-second HTTP wall), orchestrating multi-tool workflows that need persistent state, or when you want to delete custom orchestration infrastructure entirely. Any workflow involving more than three sequential tool calls is a strong migration candidate.
Stick with LangGraph or AutoGen When: Valid Reasons to Stay
LangGraph remains preferable for teams with deep existing graph logic, cloud-agnostic deployments, or workflows demanding fine-grained control over every state transition. AutoGen still leads for multi-agent simulations and research where agent-to-agent negotiation is the architecture, not a feature. See our deeper take on LangGraph vs AutoGen for the full decision tree.
Interactions API vs. OpenAI Assistants API: Decision Matrix
Both offer server-side state and tool calling. Interactions API adds background execution and multimodal fidelity controls that Assistants v2 lacks. If async long-running execution is core to your design, Google has the structural edge.
Interactions API vs. Direct Generate Content Endpoint: Migration Triggers
Migrate immediately for any workflow with more than three sequential tool calls or any session that needs to persist beyond a single request. Note that n8n and CrewAI native connectors are expected but not confirmed at GA — current integrations need manual HTTP node configuration.
❌
Mistake: Treating server-side state as a vector database replacement
Developers rip out Pinecone after seeing "memory without a database," then discover retrieval over a 50,000-document corpus degrades. Session state is execution memory, not knowledge retrieval.
✅
Fix: Keep your vector store for corpus retrieval; use Interactions API state for conversation and tool history. Run a hybrid retrieval-plus-state pattern.
❌
Mistake: Running long agents without background=True
Teams call a Managed Agent synchronously, hit the HTTP timeout, and assume the agent failed when it was still running server-side.
✅
Fix: Set background=True and supply a webhook URL for any task that could exceed 30 seconds. Poll the job ID as a fallback.
❌
Mistake: Migrating a whole LangGraph DAG overnight
Wholesale rewrites stall because complex conditional graph logic doesn't map one-to-one onto Managed Agents, and teams lose weeks debugging behavioral drift.
✅
Fix: Migrate the linear, tool-heavy sub-flows first. Keep branching control logic in LangGraph until native connectors mature.
❌
Mistake: Ignoring data residency for EU users
Server-side state means conversation data lives in Google infrastructure. EU teams assume default regions are compliant and discover residency gaps in audit.
✅
Fix: Route compliance-sensitive workloads through Vertex AI with explicit region configuration and confirm residency before storing PII in sessions.
Competitor Comparison: Interactions API vs. The AI Agent Orchestration Landscape
vs. OpenAI Assistants API and Responses API
OpenAI's Responses API is the closest structural competitor — both provide server-side state. But Google's first-class background execution has no direct OpenAI equivalent at GA, and Interactions API folds agent execution sandboxes into the same surface.
vs. Anthropic's Tool Use and Agentic Framework
Anthropic lacks a managed stateful agent endpoint as of June 2026 — Claude tool use remains stateless, requiring developers to manage context windows and tool history externally.
vs. LangGraph Cloud and LangSmith
LangGraph Cloud offers persistence and deployment but bills hosted execution separately. Interactions API bundles this into the per-token cost — a meaningful TCO difference at scale.
vs. Microsoft AutoGen and Azure AI Agent Service
Azure AI Agent Service competes on enterprise managed execution but is locked to Azure infrastructure. Interactions API spans Google Cloud and Apple's Foundation Models framework — broader reach across the device landscape.
Feature Parity Matrix: The Definitive 2026 Comparison
CapabilityGoogle Interactions APIOpenAI Responses/AssistantsAnthropic Tool UseLangGraph Cloud
Server-side stateYes (native)YesNo (stateless)Yes (hosted)
Background executionYes (background=True)No direct equivalentNoPartial
Managed agent sandboxYes (1 call, Linux)Code Interpreter onlyNoSelf-managed
Multimodal in-sessionText/image/audio/videoText/imageText/imageModel-dependent
MCP tool compatibilityYesYesYesYes
Execution billingBundled per-tokenPer-tokenPer-tokenSeparate execution fee
Full vertical stackModel→Orchestration→Agent→DeployModel + orchestrationModel onlyOrchestration only
The differentiator Google holds: vertical integration from model (Gemini) through orchestration (Interactions API) through agent execution (Managed Agents) through deployment (Vertex AI) — under one billing relationship. No competitor offers the full stack.
Industry Impact: What the Interactions API Changes for AI Development
The Death of the Custom Orchestration Layer: Who Loses
The Stateful Sovereignty Layer thesis predicts contraction in the middleware orchestration market. The same dynamic killed most third-party CI/CD tools after GitHub Actions launched natively — when the platform absorbs the assembly problem, the assemblers lose their wedge.
Coined Framework
The Stateful Sovereignty Layer as a market force
When a cloud provider absorbs agent memory and execution state, frameworks whose value was "we assemble the stateless pieces for you" lose their reason to exist. The displacement is structural, not competitive — you can't out-feature a problem that no longer exists.
What This Means for Enterprise AI Adoption Timelines
Removing infrastructure complexity from stateful agents compresses the team needed to ship a production agent from 4-6 engineers to 1-2. That's the difference between an enterprise agent program being a quarter-long initiative and a two-week sprint. Our enterprise agent adoption playbook breaks down the staffing math.
4-6 → 1-2
Engineers needed to ship a production stateful agent (est.)
[Twarx analysis of Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
2B+
Active Apple devices addressable via Foundation Models integration
[Apple, 2026](https://developer.apple.com/documentation/foundationmodels)
3+
Sequential tool calls = recommended migration threshold
[Twarx, 2026](https://twarx.com/blog/ai-agent-orchestration)
Impact on the AI Developer Tooling Ecosystem
LangChain, n8n, and CrewAI face a strategic question: their value partly relied on Google's API being stateless and needing assembly. The smart move is to become the best authoring and observability layer on top of Interactions API rather than fight the state tier.
The Apple Integration: Bringing Gemini Agents to iOS and macOS Developers
The Foundation Models framework plus Gemini cloud creates a new class of on-device/cloud hybrid agents — privacy-sensitive processing local, heavy reasoning routed to Gemini via Interactions API from Xcode.
The winners in the agent era won't be the teams with the most orchestration code. They'll be the teams who deleted theirs first.
Expert and Community Reactions to the Interactions API Launch
Developer Community Response: Praise, Concerns, and Open Questions
Early technical assessments framed the launch as "a fundamental shift from stateless text generation to stateful, autonomous workflows" — language echoing how developers reacted to OpenAI's original function calling in 2023. Concerns cluster around three areas: vendor lock-in with state stored in Google infrastructure, data residency compliance for EU developers, and pricing predictability for long-running background agents.
What AI Researchers and Commentators Are Saying
Commentators in the agent-development community described it as a paradigm shift, not an incremental update — consistent with the magnitude of the architectural change. The named authors, Ali Çevik (Group Product Manager, Google DeepMind) and Philipp Schmid (Developer Relations Engineer, Google DeepMind), positioned it explicitly as Google's "primary API" going forward.
The ADK Community: How the Interactions API Changes the Agent Development Kit
The Agent Development Kit (ADK) integration matters: ADK users gain a direct path to production deployment without swapping frameworks, since the Interactions API was designed to be ADK-native. AR and spatial computing developers see it as the missing piece for persistent spatial agents that maintain state across physical-world sessions.
The vendor lock-in concern is real but overstated. MCP compatibility means your tool definitions remain portable even when your state lives in Google. The lock-in is on memory persistence, not on tool logic — design accordingly.
What Comes Next: The Roadmap Beyond Interactions API GA
Expected Feature Additions Based on Developer Requests and GA Gaps
Google confirms "Gemini Omni (soon)" in the announcement — multimodal generation is the next named capability. Multi-region state persistence is the most-requested missing feature and the logical next infrastructure milestone.
International Availability and Regional Expansion Timeline
GA documentation now defaults to the Interactions API globally across SDKs, with Google "working with ecosystem partners to make it the default interface across 3P SDKs and Libraries" — a strong signal that ecosystem connectors are an active priority.
The Long-Term Vision and Bold Predictions
2026 H2
**Gemini Omni ships and multi-region state lands**
Google explicitly names "Gemini Omni (soon)" in the GA announcement; multi-region persistence follows standard infrastructure rollout patterns for compliance-sensitive enterprise demand.
2026 H2
**Official n8n, CrewAI, and LangGraph connectors**
Google states it is "working with ecosystem partners" — native connectors are the highest-leverage adoption accelerator and the logical next ecosystem move.
2027
**Always-on agents become Google's answer to persistent assistants**
As background execution matures, Gemini agents running continuously against live data streams become the structural counter to Microsoft Copilot's persistent assistant model.
2027
**Gemini becomes the default cloud backend for Apple on-device AI**
The Foundation Models integration targets 2B+ active Apple devices — a market large enough to reshape where heavy agent reasoning gets routed.
The roadmap signal: Gemini Omni and always-on background agents position the Interactions API as the foundation of Google's full AI platform stack.
Average Expense to Use the Interactions API
For a small business, the cost model is refreshingly simple: you pay per token, with no separate orchestration or execution surcharge. A high-volume support agent running on Gemini Flash handling a few thousand conversations a month typically lands in the low hundreds of dollars range — orders of magnitude below the cost of the 4-6 engineer team you'd previously need to build and maintain the same infrastructure. Background Managed Agent runs consume tokens for reasoning and tool steps, so budget for variable cost on long tasks. Always confirm live rates on the official Gemini pricing page, and start on the free tier in Google AI Studio to benchmark token consumption before scaling. Our agent cost optimization guide covers token-budgeting tactics in depth.
Frequently Asked Questions
What is the Interactions API and how does it differ from the existing Gemini Generate Content API?
The Interactions API is Google's unified endpoint for both single-turn model calls and multi-turn agentic workflows, announced at general availability on June 23, 2026. The key difference from the legacy Generate Content endpoint is server-side state: conversation history, tool results, and agent memory persist in Google's infrastructure rather than your database. You pass a model ID for inference or an agent ID for autonomous tasks, and set background=True for long-running work. It also adds Managed Agents (cloud Linux sandboxes), background execution, and tool combination in a single surface. Generate Content was stateless and required you to resend full history each call; the Interactions API eliminates that, along with manual session management and tool result caching.
When did the Google Interactions API reach general availability and who can access it?
The Interactions API reached general availability on June 23, 2026, announced on Google's official blog (The Keyword) by Ali Çevik and Philipp Schmid of Google DeepMind. It launched in public beta in December 2025. Access requires a Google AI Studio API key or Vertex AI credentials — both supported at GA. All Google documentation now defaults to the Interactions API across SDKs, and Google is working with ecosystem partners to make it the default interface across third-party libraries. Apple developers can also reach Gemini via the Foundation Models framework integration, callable from Xcode. The GA release brings a stable schema, addressing breaking-change complaints from the beta period.
How does server-side state in the Interactions API work, and do I still need a vector database?
Server-side state means Google persists your conversation history, tool call results, and agent memory in its infrastructure, keyed by a session ID. You no longer resend full history on each call or build a session store yourself. However, this is execution and conversational memory — not knowledge retrieval. If you need to search a large private corpus (thousands of documents), you still need a vector database like Pinecone or Weaviate. The best pattern is hybrid: keep your vector store for corpus retrieval and let the Interactions API handle session state. Mistaking session memory for a vector-database replacement is the most common early error, and it shows up as degraded retrieval quality on large document sets.
What is background execution in the Interactions API and what use cases does it enable?
Background execution is enabled by setting background=True on any call, which tells Google's servers to run the interaction asynchronously without requiring an open HTTP connection. This solves the classic 30-second HTTP timeout problem that breaks long agent runs. Use cases include deep research agents that browse and compile reports, multi-step code-generation tasks, data pipeline agents processing large inputs, and any Managed Agent that reasons, executes code, and manages files over minutes rather than seconds. You typically supply a webhook URL so Google notifies you on completion, or poll a returned job ID. Background execution is the capability with no direct OpenAI equivalent at GA, making it a key structural differentiator for long-running agentic workloads.
How does the Interactions API compare to OpenAI's Assistants API or Responses API?
OpenAI's Responses API is the closest structural competitor — both provide server-side state and tool calling. The Interactions API adds two things Assistants v2 lacks: first-class background execution for long-running async tasks, and multimodal fidelity controls spanning text, image, audio, and video in one session. It also bundles Managed Agents (full Linux sandboxes) into the same endpoint. Both ecosystems support MCP-formatted tools, so tool definitions remain portable. The decisive factor is Google's vertical integration: model, orchestration, agent execution, and deployment under one billing relationship via Vertex AI. If async long-running agents and a single per-token cost matter, Google has the edge; if you're already deep in the OpenAI ecosystem, the Responses API remains a strong choice.
What are Managed Agents in the Gemini API and how do they relate to the Interactions API?
Managed Agents are a GA feature of the Interactions API where a single API call provisions a remote Linux sandbox in which an agent can reason, execute code, browse the web, and manage files. The Antigravity agent ships as the default, and you can define custom agents with their own instructions, skills, and data sources. Think of it as AWS Lambda for agent execution — isolated, infrastructure-free, and ephemeral. They relate to the Interactions API as its agent-execution tier: you pass an agent ID instead of a model ID to the same unified endpoint. Combined with background=True, Managed Agents run autonomously without you managing servers, containers, or runtime infrastructure, which is what compresses production agent teams from 4-6 engineers to 1-2.
Should I migrate my existing LangGraph or AutoGen workflow to the Interactions API?
Migrate the linear, tool-heavy parts of your workflow first — especially any flow with more than three sequential tool calls or sessions that need to persist beyond one request. Those gain the most from server-side state and background execution. Hold off on wholesale migration if you depend on LangGraph's fine-grained conditional graph control or AutoGen's multi-agent negotiation patterns; those don't map one-to-one onto Managed Agents yet. The pragmatic path is hybrid: route stateful tool-execution to the Interactions API while keeping complex branching logic in your existing framework until native connectors mature (expected in 2026 H2). Avoid overnight rewrites — they stall on behavioral drift. Migrate incrementally, benchmark token cost on the free tier first, and confirm data residency before storing PII in sessions.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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