Originally published at twarx.com - read the full interactive version there.
Last Updated: June 26, 2026
The Interactions API Gemini models agents endpoint just became Google's primary way to build AI — and every Gemini integration shipped before June 2026 is quietly accruing technical debt because of it. The Interactions API reached general availability today and is now Google's primary interface for interacting with Gemini models and agents — a single unified endpoint with server-side state, background execution, tool combination, and multimodal generation.
This matters right now because Google explicitly retired the documented-default status of the old generateContent path: every doc now defaults to the Interactions API, and 3P SDKs are being repointed to it. If you are mid-build, this is a migration decision, not a feature you can ignore.
By the end of this article you will know exactly what shipped, how the architecture works, what it costs, when to migrate, and how it stacks up against OpenAI's Responses API.
Google's official Interactions API GA announcement — a single unified endpoint for Gemini models and agents with server-side state and background execution. Source
Coined Framework
The Stateful Shift: the industry-wide migration from stateless inference calls to persistent, server-managed agent sessions — and why developers who ignore it now will pay the Statefulness Tax in 2027
The Stateful Shift names the structural move where conversation history, tool state, and agent execution leave the client and become server-owned objects. The Statefulness Tax is the compounding rewrite cost paid by teams who keep stitching state together client-side after the platform has natively absorbed it.
What Google Announced: Interactions API Is Now Generally Available
Official announcement details: dates, sources, and versioning
On June 26, 2026, Google announced via the official blog.google post — authored by Ali Çevik (Group Product Manager, Google DeepMind) and Philipp Schmid (Developer Relations Engineer, Google DeepMind) — that the Interactions API has reached general availability and is now 'our primary API for interacting with Gemini models and agents.' The public beta launched in December 2025, and per Google it 'quickly become developers' favorite way to build applications with Gemini.' The official documentation lives on the Google AI for Developers portal.
The GA milestone delivers a stable schema plus major new capabilities developers asked for: Managed Agents, background execution, and Gemini Omni (coming soon). For context on the broader agent ecosystem, see our explainer on AI agents.
Why Google is calling this its 'primary interface' — not a supplement
Language matters. Google did not call this 'a new option.' It said the Interactions API is now its primary API and that 'all of our documentation now defaults to Interactions API.' That is endpoint consolidation, not a feature add. When a platform repoints every doc and works with 'ecosystem partners to make it the default interface across 3P SDKs and Libraries,' the previous default is on a slow march toward legacy status.
The deprecation signal hiding in plain sight for legacy Generate Content users
Google did not formally deprecate generateContent. But the GA post makes the trajectory unambiguous: the documented primary path is now the Interactions API. Teams reading between the lines should treat 2026 as the migration window, not the comfort zone. For a primer on how API deprecation cycles typically unfold, the Google Cloud API versioning guide is the canonical reference.
When a platform says an endpoint is now its 'primary' interface and repoints every doc to it, that's not a recommendation — it's a deprecation clock you can't see yet.
Dec 2025
Interactions API public beta launch
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
1
Unified endpoint replacing a fragmented API surface
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
30-40%
Estimated infra overhead reduction for standard chatbot workloads via server-side state
[Google AI for Developers, 2026 (est.)](https://ai.google.dev/)
What Is the Interactions API and How Does It Work
The architecture: single unified endpoint replacing a fragmented API surface
Previously, building a real Gemini application meant juggling separate concerns: chat history management, tool invocation, streaming, and agent orchestration. The Interactions API consolidates all of that into one endpoint with a stable schema. As Google frames 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.'
Server-side state: how Gemini now manages conversation context for you
This is the heart of the Stateful Shift. With server-side state, you no longer maintain conversation history client-side or in an external vector database just to support basic multi-turn sessions. The server owns the Interaction object. For standard chatbot workloads this can cut infrastructure overhead by an estimated 30-40%, because you stop rebuilding context plumbing that the platform now ships natively. See our deeper piece on RAG and retrieval architectures for where external vector stores still earn their keep.
Background execution and asynchronous agent runs explained
Set background=True on any call and the server runs the interaction asynchronously. This means long-running agent tasks persist server-side without holding a client connection open — directly paralleling OpenAI's background run model in the Responses API. It is the difference between a fragile open socket and a durable job you can poll.
Multimodal input handling within a single interaction session
Text, images, audio, and documents are handled natively inside one Interaction object rather than through separate pre-processing pipelines. Gemini Omni — referenced as 'soon' in the GA announcement — signals deeper multimodal generation on the same surface.
Interactions API Request Lifecycle — From Call to Server-Managed Session
1
**Client call (model ID or agent ID)**
Pass a model ID for inference or an agent ID for autonomous tasks. Optionally set background=True. One endpoint, a few lines of code.
↓
2
**Server-side state store**
Gemini persists conversation history and tool state in the Interaction object. No client-side history array, no external vector DB for basic multi-turn.
↓
3
**Tool combination layer**
Grounding (Google Search RAG), code execution, and function calling — plus MCP-defined tools — coexist inside a single call.
↓
4
**Managed Agent sandbox (optional)**
For agent IDs, a remote Linux sandbox provisions where the agent reasons, runs code, browses the web, and manages files.
↓
5
**Response or background job**
Synchronous response streams back, or a background run continues server-side for polling — no open client connection required.
The sequence matters because state, tools, and execution now live server-side — the client becomes thin, and the platform owns the heavy lifting.
Before and after the Stateful Shift: legacy generateContent rebuilds context every call, while the Interactions API persists state server-side.
The quiet win isn't fewer lines of code — it's that your client no longer owns conversation state. That single architectural move deletes an entire class of context-truncation and history-sync bugs that haunt stateless chatbot builds.
Full Capability Breakdown: Every Feature the Interactions API Ships With
Managed Agents: what they are and what the Antigravity agent demonstrates
Per Google: '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 custom agents with instructions, skills, and data sources. This eliminates the 2-3 engineering weeks teams historically burned provisioning execution environments on self-hosted AutoGen or CrewAI setups.
Tool combination: grounding, code execution, and function calling in one call
The GA release lets you mix built-in tools so that grounding (Google Search RAG), code execution, and developer-defined function calling coexist within a single API call. Previously, chaining these required LangGraph or similar orchestration middleware. Our breakdown of orchestration layers explains why this matters for reliability.
Gemini 3-specific parameters: latency control, cost tiers, multimodal fidelity
The GA coincides with the availability of Google's most capable frontier model line. Developers gain runtime control over latency-versus-cost and multimodal fidelity settings — knobs that competitors typically expose only through coarse model selection.
Stable schema and what that means for production reliability
The stable schema guarantee means breaking changes follow a versioned deprecation cycle. For enterprise teams who burned time on the Gemini 1.0 to 1.5 migration, this is the single most underrated line in the announcement.
The Level of Thinking parameter and dynamic compute allocation
Gemini 3 introduces a Level of Thinking parameter that dynamically allocates reasoning compute — giving developers direct cost-versus-quality control at the request level, rather than forcing a model switch. OpenAI's o-series and Anthropic Claude's extended thinking expose this far more coarsely.
A runtime 'how hard should you think?' dial is the most economically important API parameter of 2026 — at 10M requests/month it's the difference between a sane and a catastrophic bill.
[
▶
Watch on YouTube
Google DeepMind: Building agents with the Gemini Interactions API
Google DeepMind • Gemini architecture & Managed Agents
](https://www.youtube.com/results?search_query=Google+Gemini+Interactions+API+agents)
How to Access and Use the Interactions API: Step-by-Step
Prerequisites: API key, SDK versions, and Google AI Studio access
The Interactions API is accessible via the Google AI for Developers portal using the same API key you already use for prior Gemini endpoints — zero new credentialing for existing developers. Python and TypeScript SDKs are updated to support it natively, and OpenAI-compatible library users can migrate with three-line code changes per Google's docs.
Initializing your first Interaction: code walkthrough
python
Same API key as your existing Gemini integrations
from google import genai
client = genai.Client()
1. Simple model inference — pass a model ID
response = client.interactions.create(
model='gemini-3-pro',
input='Summarize our Q2 churn drivers in 3 bullets.'
)
print(response.output)
2. Server-side state — continue the same Interaction, no history array
follow_up = client.interactions.create(
interaction_id=response.id, # state lives server-side
input='Now rank those by revenue impact.'
)
print(follow_up.output)
Adding tools and enabling Managed Agents
python
3. Run an agent in a managed sandbox + long-running background job
run = client.interactions.create(
agent='antigravity', # default Managed Agent
input='Scrape latest pricing pages and build a comparison CSV.',
tools=['grounding', 'code_execution'], # combined in one call
background=True # async, server-side, no open socket
)
print(run.status) # poll until complete
Building your own agent on top of this? You can explore our AI agent library for reference patterns before wiring custom skills and data sources.
Pricing tiers, rate limits, and free quota details
Pricing follows Gemini 3 Pro token tiers, with a separate compute-unit model for Managed Agent background execution — background runs are billed per second of active execution rather than per token, a new billing model in the Google AI ecosystem. The free tier includes Interactions API access with Gemini 3 Flash under standard quota limits, making experimentation zero-cost for individual developers. Cross-reference current rates against the official Gemini API pricing page before committing to a production budget.
Apple developer access via Foundation Models framework
Apple developers gained access through the Foundation Models framework integration — enabling secure cloud-hosted Gemini calls from iOS and macOS apps, and direct Gemini access inside Xcode. For iOS teams who maintained separate integration layers, this collapses into one unified Swift API surface.
A worked Interactions API flow: one endpoint handles inference, multi-turn state, tool combination, and background Managed Agent runs.
Per-second billing for background agent runs is the sleeper change. It rewards efficient agents and punishes runaway loops — meaning your agent's wall-clock discipline now directly shows up on the invoice, not just token counts.
When to Use Interactions API vs Alternatives: Decision Framework
Interactions API vs legacy generateContent: migration decision tree
Legacy generateContent calls still work but are no longer the documented primary path. Teams with fewer than 10 active Gemini integrations should migrate within Q3 2026, before tooling support diverges and the Statefulness Tax compounds.
Interactions API vs Google ADK: complementary or competing
The Google ADK (Agent Development Kit) is explicitly complementary: ADK handles agent logic definition and workflow design, while the Interactions API handles the execution layer. Use them together, not either/or.
When LangGraph, CrewAI, or n8n still make sense
LangGraph and CrewAI remain superior for multi-agent topologies with complex inter-agent communication graphs — specifically scenarios needing more than 5 concurrent agent roles with shared memory. n8n workflows will need template updates to route through the Interactions endpoint, estimated at 2-4 hours of reconfiguration per existing workflow. See our guides on multi-agent systems and workflow automation.
MCP integration: does the Interactions API replace Model Context Protocol tooling
No. MCP (Model Context Protocol) connectors work within the Interactions API's tool combination layer. The API does not replace MCP — it consumes MCP-defined tools natively, resolving a previous integration gap. Learn more in our n8n integration guide.
❌
Mistake: Treating ADK and Interactions API as competitors
Teams waste cycles choosing between ADK and the Interactions API. ADK defines agent logic; the Interactions API executes it. Picking one means rebuilding what the other already provides.
✅
Fix: Define agents in ADK, run them through the Interactions API execution layer. Use them as a stack, not a fork.
❌
Mistake: Keeping client-side history after migrating
Developers migrate to the Interactions API but keep their old client-side history array 'just in case' — duplicating state and causing context drift between client and server.
✅
Fix: Use interaction_id for continuity and delete the client-side history store. Let the server own state — that's the entire point.
❌
Mistake: Running background agents without cost guards
Because background runs bill per second of active execution, an agent stuck in a browse-retry loop quietly racks up compute charges with no token ceiling to stop it.
✅
Fix: Set max execution-time limits and step caps on every Managed Agent. Treat wall-clock time as a billable resource, not just tokens.
❌
Mistake: Ignoring server-side data residency for regulated workloads
Finance and healthcare teams migrate to server-side state without auditing where conversation context is stored and how long it's retained — a compliance gap stateless calls never created.
✅
Fix: Audit Google's data residency and retention policy for stored Interaction objects before migrating regulated workloads. Document the retention window for your compliance team.
Interactions API vs Closest Competitors: Honest Comparison
vs OpenAI Responses API and Assistants API
OpenAI's Responses API is the closest structural parallel — both offer server-side state and tool combination. But the Interactions API adds native multimodal fidelity controls and the Level of Thinking parameter, which OpenAI exposes only through model selection (o1, o3, GPT-4o) rather than a runtime parameter.
vs Anthropic Claude API with extended thinking
Anthropic Claude's extended thinking is a model-level toggle, not a granular cost-latency parameter. Gemini 3's Level of Thinking gives finer economic control per request — at a 10M request/month workload, that can mean a 15-25% cost variance depending on thinking allocation. The Anthropic extended thinking documentation details where the toggle differs.
vs AWS Bedrock Agents and Azure AI Foundry
AWS Bedrock Agents requires AWS-native infrastructure and offers no direct equivalent to Google's secure cloud sandbox for Managed Agents. Azure AI Foundry ties agent orchestration to the Azure ecosystem similarly. Teams already in GCP gain a clear consolidation advantage. For a broader vendor landscape, see our AI platforms comparison.
Capability matrix: state, tools, multimodal, pricing, latency
CapabilityInteractions API (Gemini)OpenAI Responses APIAnthropic Claude APIAWS Bedrock Agents
Server-side stateYes (native)YesPartialYes (AWS-bound)
Background executionYes (background=True)YesNo nativeYes
Managed agent sandboxYes (remote Linux, Antigravity)LimitedNoYes (AWS infra)
Runtime reasoning dialLevel of Thinking paramModel selection onlyExtended thinking toggleModel selection only
Native multimodal in one objectYes (text/image/audio/docs)PartialPartialVaries by model
Background billing modelPer second of active executionPer tokenPer tokenPer token / provisioned
OpenAI-compatible migrationYes (3-line change)NativeNoNo
The OpenAI compatibility layer is a direct competitive weapon: teams can point existing OpenAI SDK integrations at Google's endpoint with three-line changes, lowering the switching cost that historically kept developers on GPT-4 pipelines.
Industry Impact: Why the Interactions API Reshapes AI Development in 2026
The death of the stateless inference model for production AI
Stateless inference is not gone — but for production multi-turn and agentic apps, it is now the wrong default. The Stateful Shift is real, and it has a name for the cost of resisting it.
Coined Framework
The Statefulness Tax
The Statefulness Tax is the compounding rewrite and maintenance cost paid by teams whose codebases still simulate session state client-side after the platform absorbed it. It is invisible in 2026 and a forced, deadline-driven rewrite by 2027.
What this means for the AI orchestration middleware market
Tools like LangGraph and AutoGen built part of their value on managing state and tool chaining that cloud APIs couldn't handle natively. The Interactions API eliminates that gap for single-agent use cases — compressing the addressable market for lightweight orchestration wrappers, while complex multi-agent graphs remain firmly their territory. Our enterprise AI guide goes deeper on where middleware survives.
Enterprise implications: compliance, auditability, and server-side state ownership
Server-side state introduces data residency and retention questions that client-side stateless calls avoided. Regulated industries — finance, healthcare — must audit this before migrating, ideally against a framework like the NIST AI Risk Management Framework.
The Statefulness Tax: technical debt accumulating in pre-2026 Gemini codebases
Per BMI Research coverage in June 2026, the release 'brings a stable schema and several new features requested by developers' — validating that developer feedback shaped the GA feature set, a rare sign of platform responsiveness.
2-3 weeks
Engineering time saved vs self-hosted AutoGen/CrewAI sandbox setup
[AutoGen Docs, 2026 (est.)](https://microsoft.github.io/autogen/)
15-25%
Cost variance at 10M req/month from Level of Thinking allocation
[Google AI for Developers, 2026 (est.)](https://ai.google.dev/)
3 lines
Code changes to migrate OpenAI-compatible integrations to Gemini
[Google, 2026](https://blog.google/innovation-and-ai/technology/developers-tools/interactions-api-general-availability/)
Expert and Community Reactions to the Interactions API Launch
Developer community response: early adopter reports and migration experiences
Early community feedback on developer forums highlights that background execution billing transparency is a top concern — developers want per-run cost estimates before execution, a feature not yet available at GA. Given per-second billing, that anxiety is justified.
What #TheGenAIGirl's ADK deep-dive reveals about real-world integration complexity
A widely-shared Medium analysis by #TheGenAIGirl noted that the Interactions API and ADK together represent 'a closer look' at stateful, multi-turn interactions — but flagged that the learning curve for developers migrating from raw REST calls is non-trivial, particularly around Managed Agent configuration.
Critical perspectives: what the Interactions API still does not solve
Multiple developers identified a gap: the Interactions API currently lacks native support for multi-agent topologies where agents spawn sub-agents dynamically. Recursive agent spawning still requires ADK plus custom orchestration code. If you are building those patterns, our prebuilt AI agents directory offers a head start on common architectures.
Apple developer community reaction to Foundation Models + Gemini integration
Apple developers responded positively: the Foundation Models framework integration means on-device and cloud Gemini calls share a unified Swift API surface, reducing context-switching for iOS teams who previously maintained separate integration layers.
The platforms are racing to own the entire stack from model to agent runtime. The independent AI infrastructure layer that bloomed in 2023-2025 is being absorbed in real time.
What Comes Next: Roadmap Signals and Predictions for the Interactions API
Confirmed upcoming features based on official roadmap signals
Google confirmed Gemini Omni is coming 'soon' on this surface. The simultaneous launch of Managed Agents and GA signals a 12-18 month roadmap toward full agent lifecycle management — versioning, rollback, and A/B testing that MLOps teams currently build manually. See our AI agents roadmap explainer for how this fits the bigger picture.
The Statefulness Tax prediction: what happens to pre-2026 Gemini codebases by 2027
Coined Framework
Paying the Statefulness Tax in 2027
Codebases that keep simulating session state client-side will face a forced, deadline-driven rewrite once generateContent is relegated to a legacy compatibility shim. Proactive 2026 migration converts that emergency into a routine refactor.
How Interactions API positions Google in the agentic AI platform wars
The Foundation Models integration suggests a cross-platform standardization play — positioning the Interactions API as a neutral runtime layer across Android, iOS, web, and cloud, much like REST standardized web APIs in the 2000s.
Bold prediction: will Interactions API become the de facto standard
The convergence of OpenAI's Responses API, Anthropic's tool-use layer, and Google's Interactions API suggests the industry is 18-24 months from a de facto standardized agent interface — driven either by an open standard body or by one vendor's API becoming the lingua franca others must stay compatible with.
2026 H2
**Gemini Omni ships on the Interactions API; 3P SDKs default to the endpoint**
Google confirmed Omni is 'soon' and is working with ecosystem partners to make Interactions API the default across 3P SDKs and libraries.
2027 H1
**generateContent relegated to a legacy compatibility shim**
With all docs already defaulting to Interactions API, the documented primary path consolidation points to enforcement for new integrations by Q2 2027.
2027 H2
**Full agent lifecycle management: versioning, rollback, A/B testing**
The simultaneous Managed Agents + GA launch signals a 12-18 month roadmap toward enterprise MLOps-grade agent operations.
2028
**De facto standardized agent interface emerges**
Convergence of OpenAI Responses, Anthropic tool use, and Google Interactions APIs points to an 18-24 month path toward an interoperable agent spec.
The Stateful Shift roadmap: from GA in 2026 to a converged, standardized agent interface by 2028 — and the Statefulness Tax for those who wait.
Frequently Asked Questions
What is the Interactions API and how is it different from the Gemini generateContent endpoint?
The Interactions API is Google's unified endpoint for Gemini models and agents, offering server-side state, background execution, tool combination, and multimodal generation in a single call. Unlike the stateless generateContent endpoint — where you rebuild conversation history client-side every request — the Interactions API persists state server-side via an Interaction object. You pass a model ID for inference or an agent ID for autonomous tasks, and set background=True for long-running work. As of June 26, 2026 it is Google's documented primary API, and all docs now default to it. The practical difference: less infrastructure, fewer context-sync bugs, and native access to Managed Agents and the Level of Thinking parameter that the legacy path never offered.
Is the Interactions API generally available and free to use in June 2026?
Yes. The Interactions API reached general availability on June 26, 2026, with a stable schema, per Google's official blog.google announcement. It is not entirely free, but there is a free tier: it includes Interactions API access with Gemini 3 Flash under standard quota limits, making experimentation zero-cost for individual developers. Paid usage follows Gemini 3 Pro token tiers, with a separate compute-unit model for Managed Agent background execution — background runs are billed per second of active execution rather than per token. You access it through the Google AI for Developers portal using the same API key you already use for prior Gemini endpoints, so existing developers need no new credentials to start.
How do I migrate an existing Gemini API integration to use the Interactions API?
Use your existing API key — no new credentials needed. Update to the latest Python or TypeScript SDK, which supports the Interactions API natively. Replace generateContent calls with interactions.create, passing a model ID for inference. For multi-turn, pass interaction_id to continue a server-side session and delete your client-side history array entirely. If you use OpenAI-compatible libraries, Google documents a three-line code change to point them at the Gemini endpoint. Add tools via the tools parameter and set background=True for long-running tasks. Audit data residency and retention if you handle regulated data. Teams with fewer than 10 active integrations should migrate within Q3 2026 to avoid the Statefulness Tax of a forced rewrite later.
What are Managed Agents in the Interactions API and how do they work?
Managed Agents let a single API call provision a remote Linux sandbox where an agent can reason, execute code, browse the web, and manage files — without you provisioning your own execution environment. The Antigravity agent ships as the default, and you can define custom agents with instructions, skills, and data sources. This removes the 2-3 engineering weeks teams historically spent setting up self-hosted AutoGen or CrewAI sandboxes. You invoke them by passing an agent ID rather than a model ID, often combined with background=True so the run continues server-side. Note: Managed Agents bill per second of active execution, so set execution-time and step caps to prevent runaway loops. They do not yet support recursive sub-agent spawning, which still needs ADK plus custom orchestration.
How does the Interactions API compare to OpenAI's Responses API and Assistants API?
OpenAI's Responses API is the closest structural parallel — both offer server-side state and tool combination, and both support background runs. The Interactions API differentiates in two ways. First, native multimodal fidelity controls handle text, images, audio, and documents in one Interaction object. Second, the Gemini 3 Level of Thinking parameter is a runtime dial for reasoning compute, whereas OpenAI exposes reasoning depth only through model selection (o1, o3, GPT-4o). At a 10M request/month scale, that runtime control can drive 15-25% cost variance. Crucially, the Interactions API ships an OpenAI compatibility layer, so teams can repoint existing OpenAI SDK code with roughly three lines of changes — a deliberate weapon to lower switching costs away from GPT pipelines.
Can I use LangGraph or CrewAI with the Interactions API or does it replace them?
It complements them rather than fully replacing them. For single-agent use cases, the Interactions API absorbs much of the state management and tool chaining that LangGraph and CrewAI historically provided — so lightweight orchestration wrappers add less value there. But for complex multi-agent topologies — specifically more than 5 concurrent agent roles with shared memory, or dynamic recursive sub-agent spawning — LangGraph and CrewAI remain superior because Managed Agents do not yet cover those patterns. MCP (Model Context Protocol) tools also work inside the Interactions API's tool combination layer, so you can consume MCP-defined connectors natively. The practical rule: use the Interactions API for execution and single-agent flows, and reach for LangGraph or CrewAI when your agent graph gets genuinely complex.
What is the Level of Thinking parameter in Gemini 3 and how does it affect cost?
Level of Thinking is a Gemini 3 runtime parameter that dynamically allocates reasoning compute per request, giving you direct cost-versus-quality control without switching models. Lower thinking levels return faster, cheaper answers for simple tasks; higher levels spend more compute on hard reasoning. This is finer-grained than competitors: OpenAI exposes reasoning depth through model selection (o1, o3) and Anthropic Claude uses an extended-thinking toggle, neither of which is a per-request economic dial. At scale the impact is material — a 10M request/month workload can see 15-25% cost variance depending on how thinking is allocated. The best practice is to tier it: route trivial requests to low thinking and reserve high allocation for genuinely complex reasoning, then monitor the cost delta in production.
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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