Steer
01 · Directability
preset
Visible controls instead of vague prompts.
80 Live Patterns
Patterns for Chat & Agents
Beyond Chat.
The future of AI interfaces
is not a better prompt.
It is a better interface.
Beyond Chat is a pattern library for AI products that do real work — chat assistants, copilots, and autonomous agents alike. The shared move: make AI steerable, inspectable, and interruptible, whether one user is typing or many agents are running.
Each pattern shows the interaction idea, the problem it solves, why the default surface — chat or naïve system — breaks down, and where the pattern should not be used.
Design Patterns for AI Beyond the Chat Interface
An animation plays here: a chat answer hides an example run of 14 steps, then breaks apart and sorts itself into this panel's four controls — steer, see, foresee, explain — and the panel takes its place among 80 live patterns. Skip or pause it with the controls below.
Manual
Anatomy of a Better InterfaceSee
02 · Observability
Plans, confidence, and state — exposed as it runs.
Foresee
03 · Predictability
will produce16:9
reproducible — same result every run
Know what you'll get before you run it.
Explain
04 · Explainability
why · search
Pulling 3 sources; the query is broad, so coverage beats depth.
Every step says why, in plain language.
search · 02 / 05
A better AI interface lets you steer it, see it, foresee it, and explain it. This panel is one — watch it, or take over.
The Problem
The interface hasn't
kept up with the work.
Chat was designed for conversation. AI now does research, plans, executes, and coordinates other agents — but the surface still asks the user to just type. The text box scales badly past one operator and one task.
Linear History
Critical context from 47 messages ago is invisible to the current task.
Hidden State
Fourteen operations happened. The interface showed one paragraph of text.
Why Chat Breaks →Three structural failure modes — for chat-first products and agent-first ones alike
Pattern Catalog
Start with the patterns.
Interface moves for AI systems that browse, reason, generate, revise, and act over time — and for the agents that orchestrate those moves on the user’s behalf. Not just one chat turn.
Inline & Contextual
16Replace brittle prompt wording with direct, local controls inside the flow of work.
Visual & Generative
14Turn outputs into visual, manipulable surfaces instead of static prose responses.
Agent Workflow
20Plan, intervene, and supervise multi-step agent runs from start to finish.
Trust & Verification
17Surface uncertainty, sources, and reasoning so users can verify before they trust.
Memory, Context & Boundary
13Make what the AI remembers, sees, and is constrained by visible and editable.
The Framework
Why, how, and what
makes control legible.
Control-aware design works on three layers: the normative intent of staying sovereign, the process that scaffolds that intent, and the interface that finally carries it.
The bottom layer — the interface — is where it becomes tangible. That is the DOPE framework, shown next.
Evaluation Lens
What good AI interfaces
need to provide.
Directability, observability, predictability, and explainability are the structural conditions that make AI autonomy legible enough to use with confidence.
This is the lens behind the archive. If a pattern does not improve one of these four dimensions, it is probably decorative AI UX rather than genuinely better interface design.
D
Directability
I can steer in real time.
O
Observability
I can see what the system is doing.
P
Predictability
I know what will happen next.
E
Explainability
I understand why it made that move.
Observability
see what the system thinks
Directability
steer in real time
Predictability
know what comes next
Explainability
understand why it decided
Stop prompting.
Start interfacing.
80 interface patterns for AI that does real work — for chat assistants, copilots, and agents alike.
Browse all 80 patterns →