After chat: designing the AI work interface

A useful product does not make a stable process

The question for a team using a conversational assistant is less whether the product will survive than whether the process can absorb changes to the model, tools and context without losing quality [1].

• Model: Anthropic launched Sonnet 5 a few months after Sonnet 4.6, bringing different capabilities, settings and costs [2][3].
• Connectors: an MCP server can change the tools and data it exposes, so a durable integration must test its contract rather than assume stability.
• Context: in Anthropic’s published example, loading many tool definitions directly used 150,000 tokens, compared with 2,000 for a code-execution design; this illustrates one optimisation, not a universal cost [5].

The design consequence is practical: chat can remain the entry point while business rules, permissions, tests and audit trails live in a layer the organisation controls.

• A model change triggers evaluation against representative cases.
• A connector change triggers schema, permission and error tests.
• A sensitive action remains bounded by code and explicit approval.

Separate exploration from operation

A general assistant already adds value by clarifying needs and handling exceptions, but repetition, criticality and volume progressively justify a more structured experience.

• Exploration: synthesise, compare, prepare a workshop or test an instruction.
• Assistance: propose a draft or analysis that the user checks within their workflow.
• Execution: trigger a bounded action with controlled inputs, permissions and a log.
• Industrialisation: measure quality, cost, lead time and rework against an identified version.

Per-seat, usage-based and outcome-based pricing can coexist; none proves a single market direction, but each forces a different link between price, work performed and observable quality.

• A seat buys capability for an identified user.
• Usage measures technical consumption, such as tokens or calls.
• An outcome requires a contractual definition of an acceptable resolution.

Chat is one work interface among several

The limitation is not conversation itself, but the gap between a generic text box and work that already has objects, states, roles and decisions.

• A travelling salesperson can dictate a note linked to the right customer and opportunity.
• A quality controller can annotate a component image and select a defect from a controlled vocabulary.
• A financial analyst can change assumptions in a visible model instead of restating them each turn.
• A technician can manipulate a diagram, preview the proposed action and confirm execution.

Generic chat Sales Production Finance Human resources Support

Chat one, generic interface

Possible interfaces Sales Voice, on the road

Production Image, quality control Finance Diagram, modelling Human resources Dashboard, steering Support Direct manipulation, technical role A chat has to speak to five jobs at once, so it never really speaks to any of them. An interface fitted to the job's own gesture replaces free text with each role's natural mode of expression.

Products already show several interface forms beyond the text response, without proving that chat will disappear or establishing any timetable for such a change.

• Google Research describes generative UI that creates pages, tools and simulations for a request, while also reporting limits in generation speed and dependence on model capability [13].
• These experiments appear in Gemini and Google Search’s AI Mode, which is documented product behaviour rather than a general forecast [13].
• AI embedded in business software follows another route by reusing the objects, permissions and state already present in the application.

AG-UI provides an open event-based protocol between an agent and a user-facing application, while MCP connects an agent to tools and data; the distinction helps separate transport, interface components and governance [17].

• AG-UI carries run lifecycle, messages, tool calls and state updates.
• Pre-built components offer stronger consistency and control.
• Assembled or generated components offer more latitude but demand stronger accessibility, security and stability testing.

Match the interface to the task

A practical portfolio usually combines conversation, guided components and bounded automation because each form serves a different category of work.

• Conversation: open research, framing, learning and rare cases.
• Guided components: frequent decisions with known inputs and outputs.
• Automation: repeatable steps with explicit conditions, exceptions and controls.

Programming has a precise role here: turn a discovered practice into an observable process before choosing its best interaction surface.

• Capture the real inputs, decisions, outputs and exceptions.
• Define measures for quality, lead time, cost and rework.
• Test the process in chat while uncertainty remains high.
• Move stable steps into dedicated components and controls.

Put human control where it matters

Useful oversight does not mean mechanically rereading every output; it concentrates human attention on high-impact decisions, exceptions and signals of uncertainty [20].

• A reversible, well-tested action can run automatically within defined limits.
• A sensitive action is previewed with its source data and expected effects.
• A consequential decision requires an authorised person, an explicit choice and a record.
• An anomaly triggers a stop, escalation or return to a known state.

The interface therefore becomes part of the control system by showing what the agent proposes, what it knows, what it will change and who can approve the action.

• Book a free, no-commitment 30-minute AI maturity audit to locate the organisation on the scale, identify its main blocker and choose a sensible first project; the call is followed by a one-page summary.

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Sources

[1] Paul-Antoine Tual, “Agentic design: why Cowork does not build durable agents”, paulantoinetual.fr, 27 August 2026. /en/blog/cowork-agents-ia-durables [2] Anthropic, “Introducing Claude Sonnet 4.6”, 17 February 2026: default model for Free and Pro plans on claude.ai and Cowork. https://www.anthropic.com/news/claude-sonnet-4-6 [3] Anthropic, “Claude Sonnet 5”, 30 June 2026. https://www.anthropic.com/news/claude-sonnet-5 [5] Anthropic Engineering, “Code execution with MCP: Building more efficient agents”, 4 November 2025: example reducing token usage from 150,000 to 2,000, a 98.7% saving. https://www.anthropic.com/engineering/code-execution-with-mcp [13] Google Research, “Generative UI: A rich, custom, visual interactive user experience for any prompt”, 18 November 2025. https://research.google/blog/generative-ui-a-rich-custom-visual-interactive-user-experience-for-any-prompt/ [17] AG-UI Protocol, official documentation: open, event-based protocol connecting an agent to a user-facing application. https://docs.ag-ui.com/introduction [20] Paul-Antoine Tual, “Engineering agentic systems: golden rules, architecture and security of the Human-in-the-Loop”, paulantoinetual.fr, 4 July 2026. /en/blog/ingenierie-systemes-agentiques-human-in-the-loop