Beyond the Chat.Agentic Interfaces Inside your Product

The Interactive Studio

We help you take your product beyond the chat.

Why the Future of Agents Lives Inside the Product

Opening a new tab to ask the AI for something is like walking into another room to turn on the light.

In modern applications, the right agent layer (agentic interfaces) doesn’t pull you away — it comes to you. Sometimes it appears as a side panel with context and approvals. Other times as contextual cues suggesting the next best action right where you’re working.

When done well, users feel like the product gives them time back. That’s the idea behind beyond the chat: an agent layer that stays in context, works with you, and provides transparency and control.

The industry has been moving in that direction for a while. Notion 3.0 doesn’t just write — its Agents act within your workspace to plan launches, break down tasks, and generate documentation. All inside the product, not in a separate conversation. The design lesson is clear: visible state, role-based approvals, and auditable results without mental jumps.

OpenAI is pushing the concept of building agents as workflows in a visual canvas (Agent Builder and AgentKit). Seeing inputs, outputs, versions, and traces makes AI readable and governable across product, legal, and tech. That “engine room” enables the right interface: why it proposes something, what it’s about to execute, and how to roll it back if it doesn’t fit.

In customer support, Intercom embeds its agent Fin directly into the help center: the copilot suggests a reply based on your knowledge base; the human edits, approves, and sends. No black boxes — every suggestion’s origin is visible. This is the real agent-with-UI pattern, not another chat window.

Across broader suites, Microsoft 365 (with Agent Store) and Google Workspace (with Gemini Gems) bring intelligence to the side panel and contextual actions in Outlook, Teams, Docs, Sheets, or Gmail. The message is consistent: don’t open another app to talk to AI — let AI sit beside you and work within your flow.

Design Signals: Figma, Adobe, Canva, and Other Major Platforms

Figma is pushing on two fronts: Figma AI for searching and generating within files, and opening to agents through MCP and Make. Agents can access document context and propose changes without breaking flow — actions on the canvas and from the side panel.

Adobe brings AI into everyday work with Acrobat AI Assistant for summaries, Q&A, and formatting inside Reader and Acrobat. In marketing, AI Agents in Experience Cloud orchestrate agents, Firefly, and customer data with a focus on governance and traceability.

Canva is accelerating with Magic Studio and connectors that let you create assets from the editor without breaking context. You generate, see impact, and adjust — AI working beside you on the same canvas.

2025–2026 Trends Already in Motion

Multimodal and Contextual Agents
Agents now understand text, images, audio, and even video — and act right where you work, without taking you out of flow. Notion 3.0 introduces Agents that complete tasks end to end inside your workspace. OpenAI launches AgentKit to design, version, and audit agents as governed workflows. Google brings its Gems to the Workspace side panel. Microsoft deploys an Agent Store to discover and use agents directly within 365 and Teams.

Dynamic Personalization and Collaboration
Agents learn from preferences and telemetry to tailor their suggestions to each team. In Intercom, Fin proposes and the person edits and approves. In Figma, AI and Make open guided actions on the file. Adobe integrates agents into Experience Cloud and a document assistant into Acrobat. Canva brings AI closer to the editor through Magic Studio.

Explainability and Visual Feedback
The best-performing products show inputs, outputs, versions, and traces. This reduces friction and supports compliance. AgentKit and Workspace side panels follow this pattern.

Applied Psychology: Trust, Load, and Control

Trust in intelligent systems is rhythmic, not dogmatic. When an algorithm fails visibly, many users tend to abandon it faster than a human. On the other extreme, there’s automation bias — we trust too much and stop verifying. The designer’s job is to calibrate that trust, not maximize it.

Calibration starts by making the agent’s intent legible. A short, actionable explanation works better than a cryptic paragraph: “I recommend pausing these 3 campaigns due to a 30-day ROAS drop. I can prepare a plan B.” If we also show a confidence indicator and offer comparable alternatives with estimated impact, the user can decide without leaving the flow. This fits neatly into a side panel or an inline microinteraction that appears where the task happens.

Cognitive load drops with clear steps and progressive disclosure. The Review → Adjust → Approve pattern avoids chained modals: start with the recommendation and its impact; reveal details on expand. When users understand why, can intervene, and have a safety net, the agent becomes a tool, not a promise. This is where mission control (agent control panels) shine — readable timelines of what, when, why, and with which data the agent acted.

Attention rhythm also matters. Progress is easier with a tangible goal: a progress bar and “last step” message help closure. If a user leaves mid-action, on return they need an anchor: “Pick up where you left off.” These work both in panels and inline (progress chips or visible steps).

[Suggested image: Compact card showing “High confidence,” mini “because…,” buttons “Adjust” and “View alternative.” Below, a progress bar with “Last step” and a soft banner “Pick up where you left off.”]

To keep the pattern consistent, define micro-interaction rules that apply across the product:

Before executing, always show impact preview and an edit option.
If a suggestion is ignored, the UI silences itself for a reasonable time to preserve focus.
After execution, provide a true undo and quick access to details and data sources.

Major AI Platforms and the Lost-Context Dilemma

At The Interactive Studio, we’ve been watching how major AI platforms accelerate integration with external apps so users never have to leave their environment. ChatGPT, Claude, Copilot, and Gemini now execute tasks, connect services, and use third-party tools without leaving the main interface.

While this brings clear gains in fluidity and productivity, it introduces a dilemma product designers can’t ignore:

What happens to the context, identity, and experience of your own product and brand?
When recommendations and decisions happen within a generic interface, your product’s personality dilutes. The user gains efficiency but may lose brand signals:

The original flow’s continuity fragments.
The “glue” between systems becomes invisible work (copying, re-explaining, moving data).
Brand perception weakens when the main context is no longer yours.

Our Design View
At The Interactive Studio, we believe the challenge is to design agents that extend your products, not replace them. Integrating AI shouldn’t displace your identity — it should reinforce it: open protocols, shared contexts, and a UI layer that preserves your brand’s tone, aesthetics, and logic, even when connecting to external systems.

The real opportunity lies in combining the best of both worlds:

The fluidity and power of large AI platforms.
The coherence and unique value of your own product.

The future won’t belong only to universal agents — but to agents with identity, built to understand where they are and with a clear purpose.

Europe, Agents, the AI Act, and Beyond

The AI Act demands effective human oversight, traceability, and clear controls. Translate that into visible patterns: role-based autonomy levels, understandable warnings before sensitive actions, and an accessible safe stop button (kill switch). All within panels and microinteractions.

Audit-Oriented Design
Agent control panels with readable timelines; impact previews before execution; true undo and A/B comparison with estimates. These elements don’t just comply — they build trust and drive adoption.

Other Regulations That Shape Design

TL;DR Designing an agent isn’t just about UI. In Europe, asking for permissions honestly, leaving a trace of actions, being accessible, and withstanding incidents are all part of design. These rules affect copy, flows, components, and telemetry.

GDPR — Principles and Automated Decisions

Key points: lawfulness, minimization, purpose limitation, demonstrable accountability. If decisions with legal or similar effects are made solely by automation, they require reinforced transparency and human intervention paths (Art. 22).
Design impact: clear summaries of purpose and legal basis; in-context data editing controls; clear explanations when a recommendation comes from models and how to request human review.

ePrivacy (cookies/trackers) — Real Consent, No Dark Patterns

Key points: prior, informed, granular consent for anything non-essential; ability to withdraw consent as easily as it was given; proof of consent.
Design impact: purpose-based preference panels (no prominent “Accept all” and hidden “Reject”); persistent access to change preferences; avoid dark patterns (see also DSA).

DSA (Digital Services Act) — Dark Patterns and Transparency

Key points: ban on dark patterns that distort decisions; transparency for recommendation and ad systems.
Design impact: neutral text and symmetric options; no extra friction when rejecting; “why you’re seeing this” explanations in recommendations and visible system settings.

NIS2 — Security and Incident Notification

Key points: risk management and prompt notification of significant incidents to authorities and affected users.
Design impact: banners or status centers for incidents; visible security change logs; remediation paths if the agent is restricted for safety reasons.

Data Act — Product and Service Data Access/Portability

Key points: enable users and businesses to access data in structured, machine-readable formats; share personal and non-personal data under clear conditions.
Design impact: self-explanatory exports; webhooks/export tasks from the agent control panel; data provenance labels for decision-making.

European Accessibility Act (EAA) — Accessibility by Law (Already in Force in Spain)

Key points: accessibility requirements for websites/apps, e-commerce, banking, transport, etc. In Spain, effective June 28, 2025 (Law 11/2023), with a transition until 2030 for existing services.
Design impact: adequate contrast, visible focus, keyboard navigation; agent states announced with ARIA live regions; confidence/impact descriptions not color-only; conformance documentation.

Cyber Resilience Act (CRA) — Secure by Design for Software Too

Key points: cybersecurity requirements throughout the lifecycle for products with digital components (including software), vulnerability management, and updates.
Design impact: visible reporting channels; agent version history with security notes; alerts when an action is blocked by policy.

How to Integrate AI Agents Without Rebuilding Your Product (and How to Design Them from Scratch)

The key isn’t choosing a single pattern but applying each resource where it delivers the most value. For existing products, start small and measurable. For new products, that same logic becomes repeatable patterns.

In Existing Products: Integrate Without Friction

Pick a low-risk routine (generate a report, clean a dataset, regroup incidents). Decide how to present it based on the decision type:

Inline microinteraction
For short, frequent decisions. A contextual card with a recommendation, a one-line rationale, and a clear action (accept/adjust). It hides after confirmation or silences if ignored.
Side panel with agent control panel
For flows involving multiple steps, permissions, or audits. Show state, rationale, role-based approval inbox, and history.

Roll out to cohorts with feature flags and add metrics: time saved per task, human correction rate, adoption, and satisfaction. With evidence, increase autonomy by role and threshold. Routine actions can auto-execute with confirmation; sensitive ones require approval and true undo. This approach aligns with Notion Agents, Intercom, and suites like Microsoft and Workspace: the agent lives in context, performs small actions, and asks permission when needed.

In New Products: Objectives and Repeatable Patterns

From day one, we work with objectives (e.g., “reduce campaign costs by 15%”), not just tasks. That forces clarity on how the agent decides and how the user sees it:

Reusable UI patterns: A/B comparator with estimated impact, confidence card, “pick up where you left off,” approval inbox, agent control panel, microinteraction library.
Deterministic interface layer that orchestrates state, data, and events. AI assists, not monopolizes. Every action leaves a trace. Users can edit before and undo after.
Orchestration and tooling: designing agents as flows with inputs, outputs, and versions makes them easier to map to UI. This fits Agent Builder and, operationally, tools like n8n, Make, or Zapier. Thinking in runs, inputs, outputs, and versions from the start makes the interface more legible.

Quick Example (4 lines)
Catalog duplicate consolidation. An inline microinteraction suggests merging 3 entries with medium confidence (−6% stock error). The user reviews the diff in the agent control panel and approves. Result: −18% rework in support after 3 weeks.

Microcopy for Coherence

“Here’s what the agent understood. Here’s what it will do. Expected impact: X. Adjust here. Undo here.”
“Prefer route B? Estimate: +2 days / −8% cost. I’ll prep it and notify you for approval.”

Pilot Roadmap in Just a Few Weeks

If it looks transparent and acts transparent, adoption follows. When you turn the AI Act into clear controls, the result is trust and speed. That’s what beyond the chat really means today: AI at your side, inside your product.

Discovery: choose 1–2 real routines and metrics.
Prototyping in Figma: side panel with actionable summaries and role-based approvals.
Pilot: feature flags, cohorts, telemetry.
Evaluation: increase autonomy if data supports it.

Side Panel vs. Microinteractions: Quick Guide

Situation	Best Pattern	Reason	Main Metric
Short, frequent decisions	Inline microinteraction	Less friction, more focus	Acceptance rate and time per action
Long or auditable flows	Side panel with agent control panel	Context, permissions, and traceability	Corrected incidents and rework
Sensitive actions under the AI Act	Panel with role confirmation	Oversight and audit trail	Prevented alerts and clean audits

Frequently Asked Questions

What are agentic interfaces?
Interfaces with agents that act in context, inside the product. Not just chat — with transparency, control, and traceability.
How can I start without rebuilding my app?
Pick a low-risk routine. Compare microinteraction vs. side panel. Measure time saved and adoption. Scale if it works.
How does the AI Act affect design?
Define autonomy levels by role. Add understandable warnings. Record traceability. Offer true undo and an accessible safe stop button.

At the end of the day, we all need technology to give us time back. AI that understands context, proposes clear actions, and keeps control in the user’s hands elevates the experience. Agentic interfaces work when they come with transparency, control, and traceability.

Want to try it in your own product?
At The Interactive Studio, we help you launch a pilot at the speed you need.
We analyze your real workflows, suggest suitable routines, and design the agent control panel in Figma, following your brand guidelines and design system.
If needed, our FrontEnd team can integrate it directly into your app.

Insights

Beyond the Chat.
Agentic Interfaces Inside your Product