Executive Summary
Agentic design transforms products from static tools into adaptive, intelligent experiences. By layering proactive, outcome-aware interaction patterns onto familiar interfaces, teams reduce user friction, boost engagement, and unlock measurable gains in efficiency, trust, and satisfaction; without overhauling the UI or requiring deep AI infrastructure.
This framework equips product and engineering teams to spot high-impact opportunities, apply modular agentic patterns, and build software that feels one step ahead.
Agentic Initiative Framework
A reference standard for designing intelligent, outcome-driven product experiences using agentic interaction patterns. This framework supports product and engineering teams as they shift from static user flows to dynamic, AI-enhanced workflows.
Agentic patterns are defined in section “8. Agentic Patterns” near the end of this document.
Quick Start for Product Teams
Agentic design is a shift from building static, linear user flows to crafting adaptive, outcome-aware experiences. Instead of asking users to click through predefined steps, agentic interfaces use intelligence to guide, prompt, and react in ways that feel human, helpful, and efficient.
As a product manager, you don’t need to be a machine learning expert to get started. What matters most is identifying high-friction areas in your product, moments where users get stuck, hesitate, or forget, and imagining how an intelligent, conversational layer might resolve that moment faster and with less effort. This guide gives you patterns, prompts, and examples to start introducing agentic behavior in ways that tie directly to business outcomes like increased activation, reduced support volume, or higher NPS.
Introduction
Why Agentic Design Matters
Traditional UX relies on fixed flows, menus, and page transitions. But as user expectations shift toward intelligent, proactive support, static designs fall short. Agentic design introduces interactive patterns that feel responsive, predictive, and conversational; tailored to intent and outcome, not just clicks and screens.
Agentic systems can reduce friction, guide decisions, and adapt over time. They also offer a powerful way to introduce new capabilities subtly, weaving change into familiar flows without disorienting users. They elevate digital experiences by making software feel more like a smart assistant than a passive tool; one capable of integrating multiple applications into a single interface, blurring boundaries between systems and hiding the seams of decoupled services.
Static Flows vs. Agentic Patterns
Static flows are linear, brittle, and optimized for the average path. They assume users know what they want and how to get it. Agentic patterns, by contrast, recognize ambiguity, anticipate forks, and support flexible paths. They are powered by real-time data, memory, and conversational interaction.
Agentic interaction doesn't replace UI; it overlays intelligence on top of it. This enables systems to:
- Guide without forcing
- Act without guessing
- Adapt without retraining users
The shift is not about replacing UX, but augmenting it with patterns that create dynamic, context-aware pathways.
Core Principles
Outcome-Oriented Interaction
Agentic design starts with the end in mind. Every interaction is shaped by an intended outcome; not just by user clicks or page views. This principle ensures agents prioritize purpose over process.
Context Awareness
Effective agents maintain awareness of user intent, session state, time, and application context. This allows interactions to feel timely, appropriate, and adaptive across sessions and tools.
Reversibility & Trust
Agents should be safe to interact with. Reversibility: the ability to undo, clarify, or reframe actions; builds user trust and reduces fear of error. Every move should feel correctable.
Minimum User Burden
Agentic flows should reduce, not add to, user cognitive load. This includes minimizing unnecessary prompts, guessing well, and offering clear paths forward.
Seamless System Integration
Agents often span multiple systems, APIs, or apps. A core principle is the ability to abstract and coordinate across those services; making multiple tools feel like one unified experience.
Outcome Definition & Validation
Outcomes must be more than user intent; they require observable, testable application state. A well-defined outcome includes specific data conditions that must be met to declare success. For example:
- "Invoice sent" → invoice status updated, delivery timestamp stored, confirmation from downstream system received
- "Booking complete" → record created, payment processed, confirmation ID generated
Defining outcomes with state fingerprints ensures agents can:
- Detect when an outcome is met
- Retry or escalate if not
- Support verification and testing in code
Outcomes that span systems must also enforce state consistency. Agentic systems may need to use a distributed logical commit model or post-commit verification to ensure all linked systems reflect the same truth. This helps prevent phantom states (e.g., showing "booked" when a payment failed) and supports robust recovery if something breaks mid-flow.
Use patterns like Ask + Confirm and Track + Remind to support both initiation and verification of outcomes across decoupled services.
Implementation Guidance
Identifying Agentic Moments
Look for friction, ambiguity, or repetitive behaviors; these are prime opportunities to introduce agentic patterns. Good candidate moments include form inputs, multi-step workflows, forgotten follow-ups, and points of decision fatigue.
Ask:
- Is the user unsure, stuck, or overloaded?
- Is this a moment where guidance, memory, or anticipation would help?
Decomposing Experiences into Agentic Flows
To implement agentic patterns, start by breaking down workflows into discrete user outcomes. Then, identify points of friction, ambiguity, or delay that disrupt momentum. These are ideal opportunities for agentic intervention.
Step-by-step Decomposition:
- Start with the outcome
- What must be true in the system when this is complete?
- Define observable data state and confirmable result.
- Trace the journey backward
- What steps, inputs, and decisions precede the outcome?
- Where do users get stuck, forget, or hesitate?
- Map the friction and ambiguity
- Look for uncertainty, repetition, delays, or high abandonment points.
- Apply patterns deliberately
- Ask + Confirm → clarify and verify
- Recommend + Explain → reduce cognitive load
- Track + Remind → maintain flow
- Instrument and validate
- Define checkpoints and triggers that help agents track progress and verify success
Example:
Outcome = "Loan Application Submitted"
- Required state: all fields validated, document uploaded, user received confirmation
- Friction: confusion in final step, forgotten attachments
- Flow = Ask + Confirm → Recommend + Explain → Watch + Wait → Track + Remind
This approach ensures that agentic flows are grounded in outcome-based thinking and tailored to real user experience breaks.
Framing Agentic Work for Business Impact
Agentic flows are most effective when explicitly tied to strategic goals. Rather than framing these experiences as UX enhancements alone - position them as drivers of measurable business impact.
Use the following structure to anchor agentic design in business value:
Common Strategic Objectives
- Reduce support tickets
Example: Proactive recommendations and confirmations prevent user errors. - Increase task completion rates
Example: Agents nudge users past known drop-off points with Track + Remind. - Accelerate onboarding or setup
Example: Handoff + Resume helps users complete multi-session workflows. - Boost customer trust and satisfaction
Example: Reversible flows and clear agent logic reduce perceived risk. - Enable self-service in complex flows
Example: Recommend + Explain guides users through high-friction decisions.
Mapping Agentic Outcomes to Metrics
- Define success using observable state (e.g., % completed flows, time to resolution).
- Use A/B testing to compare assisted vs. unassisted outcomes.
- Capture reversibility events (how often users do undo/correct agent moves).
- Track agent engagement (e.g., prompt accept rate, reminder follow-through).
Strategic Framing Tips
- Frame agents as solving a real, costly problem; not just as assistants.
- Use CX or operational metrics to validate their impact.
- Highlight trust, consistency, and user flow retention in internal comms.
Pattern Selection Matrix
Each pattern serves a unique purpose. Use the following shorthand to match pattern to need:
- Ask + Confirm: Clarify and verify intent before acting
- Recommend + Explain: Suggest paths with context
- Watch + Wait: Wait for the right trigger before stepping in
- Track + Remind: Monitor intent and nudge at the right time
- Handoff + Resume: Enable flow continuity across sessions
- Multi-Intent Fork: Clarify ambiguous input or next step
Layering is encouraged; a flow might begin with Ask + Confirm and later switch to Track + Remind.
Common Pitfalls to Avoid
- Over-assistance: Agents that interrupt too often or ask redundant questions
- Memory gaps: Losing session state, forgetting user preferences
- Opaque logic: Acting without clear justification or explanation
- One-size-fits-all: Using a pattern where it doesn't fit just to feel "agentic"
Successful implementation is less about novelty and more about fit, restraint, and timing.
Design Artifacts & Language
Agentic Initiative Planning Template
This planning template helps teams frame and communicate agentic work within broader product goals. It’s useful at project kickoff or when introducing agentic behavior into an existing flow.
1. Initiative Summary
- What you're building and why now is the right time
2. Strategic Objectives
- Clear outcomes linked to business or customer impact
3. Initial Use Cases
- Where the agentic experience appears and how it behaves
4. Design + UX Impacts
- How interface elements or flows will change
5. Engineering Considerations
- Major systems, APIs, or architecture changes required
6. Dependencies
- Data, teams, or infrastructure needed to execute
7. Success Criteria
- Metrics to track the impact of the new behavior
8. MVP and Validation Plan
- Pilot scope, success thresholds, and how learning will be captured
Use this template as a starting point, blending it into your team's workflow or existing planning models.
Prompt & Response Templates
Well-crafted prompts are essential to agentic flow. They should be brief, clear, and reflect the user’s current context. Good prompts:
- Set expectations
- Offer a choice or direct action
- Avoid ambiguity
Example Template:
- Prompt: "Want me to summarize the meeting now or later?"
- Expected Response: "Later, remind me tomorrow."
Use consistent voice and tone across agents to establish trust and reduce friction.
Sample Interaction Flows
Pattern combinations can be mapped as stateful flows. For example:
- Ask + Confirm: Collects user intent
- Recommend + Explain: Offers best path forward
- Handoff + Resume: Supports pause and continuation
These mini-flows can be prototyped using journey maps or flow diagrams, with agent roles annotated.
Pattern Diagnostic Checklist
To apply a pattern effectively, test against these:
- Is this moment ambiguous, interruptible, or repetitive?
- Is agent memory required or helpful?
- Would this action benefit from clarification, confirmation, or explanation?
- Is the agent guiding, reacting, or reminding?
This checklist supports both design and QA teams in validating the correct use of patterns.
Future Directions
Managing Multiple Outcomes in a Single Interface
Agentic interfaces often need to support many distinct outcomes at once. This introduces implementation friction; especially when different outcomes require unique data, validation, or sequencing.
There are two primary models to manage this:
1. Outcome Swimlanes
Each outcome is treated as a distinct lane with its own triggers, logic, and agent state. This model simplifies reasoning, reduces interference between outcomes, and enables parallel execution.
2. Dynamic Outcome Mashups
Outcomes are surfaced and prioritized contextually within a shared interface. Agents dynamically interpret injected outcome strategies and adjust flow accordingly. This approach offers flexibility but increases complexity and state coordination.
Agentic Strategy Injection
The agentic framework must support receiving outcome definitions; including their data requirements, completion criteria, and intent signals; as structured payloads. When injected, these outcome strategies become actionable without additional hardcoded logic.
A robust agentic implementation can:
- Recognize when a specific outcome is relevant
- Activate the necessary sub-patterns
- Track and validate progress toward the injected goal
This enables high extensibility; allowing product teams to introduce new agentic behaviors by defining outcomes declaratively, rather than rewriting flows manually.
Composability of Patterns
The real power of agentic interaction emerges when patterns are chained or composed. Rather than standalone moves, agentic flows can shift dynamically across patterns; such as starting with Watch + Wait, then surfacing a Recommend + Explain, and ending with Track + Remind.
Tooling and design systems will evolve to support this pattern choreography, allowing teams to define, simulate, and optimize hybrid agentic flows.
Coordination Across Agents
As systems scale, multiple agents may operate within the same experience. Coordination; between agents or across sessions/devices; becomes a vital capability. Future agentic frameworks will need protocols for:
- Turn-taking between agents
- Shared memory models
- Conflict resolution across recommendations
Agentic UX Metrics
Classic UX metrics (clicks, conversions) fall short in measuring agentic success. New metrics will emerge, such as:
- Time to resolution with agent assist
- Frequency of trusted agent interaction
- Reversibility rate (how often users undo or revise agent actions)
- Pattern adoption rate by scenario type
These will help teams quantify the effectiveness of agentic experiences and refine their use.
Solution Framing: Agentic Outcome Modules (AOMs)
To support flexible and safe injection of new outcomes, the Agentic Framework can define modular payloads known as Agentic Outcome Modules (AOMs).
Each AOM includes:
- Outcome identity and goal
- Required input and validations
- Completion criteria and success state
- Preferred pattern sequences (e.g., Ask + Confirm → Track + Remind)
- Optional prompts or UI extensions
AOMs function like plug-and-play outcome strategies. They are designed for:
- State sandboxing: Scoped data boundaries to avoid interference
- Side effect transparency: All writes and reads are declared and observable
- Execution independence: No implicit dependencies on other AOMs
- Runtime activation: The agent determines when and how to engage the AOM based on context
This model promotes modularity, testability, and safe extensibility; enabling product teams to ship new intelligent behavior without breaking or rewriting existing flows.
Classic UX metrics (clicks, conversions) fall short in measuring agentic success. New metrics will emerge, such as:
- Time to resolution with agent assist
- Frequency of trusted agent interaction
- Reversibility rate (how often users undo or revise agent actions)
- Pattern adoption rate by scenario type
These will help teams quantify the effectiveness of agentic experiences and refine their use.
Toward Operational Resilience in Agentic Systems
Agentic interfaces are dynamic by design; but their operations must be stable, maintainable, and extensible over time. As product teams evolve experiences, the risk of unintended entanglement between injected outcome modules increases.
To manage this, the framework must eventually support:
- Outcome encapsulation: Injected outcomes (AOMs) must operate in isolation unless explicitly declared otherwise
- Outcome dependency graphs: When interactions between outcomes exist, they must be modeled and visible
- Impact simulation: Teams should be able to simulate changes to one outcome without breaking others
- Agentic rollback and versioning: Outcomes must be independently testable and revertible at runtime
This area remains underdeveloped. Solving it would unlock a future where product teams can continuously deploy new outcome logic with minimal revalidation, allowing agentic systems to adapt at the pace of product iteration.
Conflict Handling & Interruptibility
As agents become more capable, multiple flows may compete for the user’s attention or context. The system must handle interruptions and conflicts gracefully.
Common conflict scenarios:
- A Track + Remind flow interrupts an Ask + Confirm
- Two outcomes are partially complete and offer guidance simultaneously
- User input is ambiguous and could trigger multiple flows
Recommended model:
- Interruptible patterns: Each pattern defines whether and how it can be paused or overridden
- Priority routing: Agent selects or defers based on urgency, outcome priority, or user signal
- Explicit deferral: “Want to finish what you started first, or switch?”
Agents should handle interruptions in ways that feel natural and user-first, with an emphasis on clarity and recoverability
Agentic Memory Models
Agentic systems rely on memory to feel responsive, adaptive, and context-aware. But memory must be designed deliberately; not all information should persist forever, and not all agents should share what they know.
Memory scopes to consider:
- Ephemeral memory: Context that exists only for the duration of a pattern (e.g., one Ask + Confirm)
- Session memory: Temporary information held across a conversation or session (e.g., onboarding session)
- Long-term memory: Persistent knowledge of preferences, outcomes, or behaviors over time
Key principles:
- Memory should be scoped and transparent to the user
- Systems must define how memory is updated, expired, or forgotten
- Memory enhances the ability to resume, recommend, and adapt; but must respect reversibility and trust
Designers and engineers should ask: What does the agent need to remember? For how long? And how does it influence future behavior?
Agent-to-Platform Translation
To make agentic patterns reusable, portable, and scalable, they must translate well across platforms; from web to mobile to voice. This will likely drive:
- Agentic markup standards
- Platform-specific rendering toolkits
- Pattern abstraction layers for cross-platform behavior
Agentic Governance & Standards
As agentic interfaces scale across teams and systems, governance becomes essential to ensure consistency, safety, and accountability. Without governance, agent behaviors may drift, conflict, or introduce regressions.
Key governance areas:
- Outcome Registration & Approval
Each AOM (Agentic Outcome Module) should be registered with metadata: owner, scope, dependencies, validation criteria, and change history. - Pattern Usage Guidelines
Organizational standards should clarify when each pattern is appropriate and what user types or contexts to avoid. - Prompt & Response Standards
Define tone, structure, and fallback language for prompts. Include content sensitivity, cultural neutrality, and maximum length guidelines. - Memory & Data Boundaries
Set rules for which agent types can retain what memory scopes; ephemeral, session, or long-term; and for how long. - Change Management
Require testing and documentation for changes to existing outcomes. Include versioning, rollback, and sandbox support. - Auditability
Log all agentic decisions and reversals with contextual metadata to support debugging, transparency, and compliance.
With lightweight, declarative governance in place, agentic systems can scale safely; balancing agility with trust.
To make agentic patterns reusable, portable, and scalable, they must translate well across platforms; from web to mobile to voice. This will likely drive:
- Agentic markup standards
- Platform-specific rendering toolkits
- Pattern abstraction layers for cross-platform behavior
Example Scenario: Employee Onboarding Assistant
Context: A company wants to simplify its multi-step onboarding flow for new hires; collecting info, guiding setup, and ensuring all systems are activated across HR, IT, and Payroll.
Outcome: “Onboarding Complete”
Success Criteria (state-based):
- HR forms submitted
- Laptop requested
- Payroll info verified
- Slack account created
- Manager notified
Agentic Pattern Flow
- Watch + Wait
Agent detects new hire record created in HR system. - Ask + Confirm
Agent: "Welcome aboard! Want help setting up your employee profile?"
User: "Sure."
Agent: "Let’s start with contact details; ready?" - Recommend + Explain
Agent: "Most new hires opt to auto-fill from LinkedIn. Want me to try that?" - Track + Remind
Agent sets gentle nudges: - “Still need your banking info to finish payroll.”
- “Want me to ping you tomorrow if it’s not done?”
- Handoff + Resume
Mid-way through IT setup, user exits. Agent stores state.
Later: “You left off at choosing your laptop; want to finish that now?” - Multi-Intent Fork
User says: “I need access.”
Agent: “Slack, Email, or GitHub?” - Final Validation (Outcome Detection)
Agent confirms success by checking: - HR + IT + Payroll flags = ✅
- Sends: “All set! You’re officially onboarded”
Design Notes
- Each sub-outcome (e.g., laptop request) could be modeled as its own AOM
- Swimlane isolation allows testing each piece independently
- Cross-system triggers are abstracted via agentic orchestration
- Prompts evolve naturally as user progresses
Conclusion
The Agentic Initiative Framework offers a practical language for teams building intelligent, outcome-first experiences. One of the quiet superpowers of agentic design is its ability to introduce new capabilities without disruption, gently weaving change into familiar flows. Rather than requiring retraining or announcements, agentic interfaces let software evolve in place, surfacing just-in-time utility that feels natural, even expected. These six patterns serve as modular building blocks: each suited for specific contexts, yet flexible enough to compose into larger, adaptive flows.
This isn’t a prescription. It’s a toolkit. What matters most is how teams use these patterns with intention; minimizing burden, maximizing clarity, and amplifying the value of software by making it feel aware, responsive, and genuinely helpful.
As you design and implement, return to the core principles: outcome-orientation, context-awareness, trust, and seamless integration. With thoughtful application, agentic systems can help users achieve more with less effort; and help teams deliver experiences that feel one step ahead.
Agentic Patterns
Quick Start on Patterns for Product Teams
Agentic patterns are modular interaction strategies that help products feel more intelligent, helpful, and intuitive; without requiring full AI transformation. Each pattern is built around a common user experience moment: decision hesitation, missing context, forgotten tasks, or ambiguous input. Rather than prescribing a single solution, these patterns offer repeatable moves that work across tools and teams.
Think of them as UX ingredients. You don’t need to apply all of them, and most flows use only 1–2 at a time. But when chosen intentionally, they reduce friction, improve task success, and create the perception of smart, responsive software. You’re not designing for flash; you’re designing for momentum.
Pattern 1: Ask + Confirm
Definition:
Agent prompts the user for a specific piece of information and confirms it before proceeding.
Best Used When:
- User input is required to trigger or refine an action
- Accuracy matters before commitment
Behavior:
- Prompts are clear and scoped
- Agent confirms with lightweight summary before proceeding
Example:
Agent: "What date should I schedule your meeting?"
User: "Next Friday."
Agent: "Got it; that’s July 5th. Confirm?"
Fit Criteria:
- Low ambiguity
- Confirmation adds trust or prevents error
Dependencies:
- Real-time response capability
- Context memory for short-term dialogue
Anti-Patterns:
- Re-asking after confirmation
- Confirming vague or irrelevant details
Pattern 2: Recommend + Explain
Definition:
Agent offers a recommendation with rationale, optionally seeking confirmation or override.
Best Used When:
- Users benefit from guidance or narrowing choices
- Transparency builds confidence
Behavior:
- Recommendation is paired with a "why"
- User can accept, reject, or modify
Example:
Agent: "You’ve run 4 meetings at 3pm. Want me to suggest that time again? It seems to work well."
Fit Criteria:
- Recommender logic exists
- Decision quality improves with assist
Dependencies:
- Access to history or preference signals
- Justification engine or text templates
Anti-Patterns:
- Unexplained recommendations
- Recommending without opt-out or override
Pattern 3: Watch + Wait
Definition:
Agent passively observes context, behavior, or environment until a trigger event occurs.
Best Used When:
- Action depends on external or user-driven event
- Interrupting would reduce flow
Behavior:
- Silent observer until trigger fires
- Reaction feels timely and relevant
Example:
Agent watches a document. When user stops editing for 10 minutes, it prompts: "Ready to summarize this?"
Fit Criteria:
- Clear, detectable trigger condition
- Non-urgent context
Dependencies:
- Event monitoring or sensor hooks
- State memory
Anti-Patterns:
- Polling too aggressively
- Triggering on ambiguous signals
Pattern 4: Track + Remind
Definition:
Agent monitors an intended action or outcome and reminds the user when needed.
Best Used When:
- Tasks are time-based or easily forgotten
- Users value nudges over nagging
Behavior:
- Creates reminders or checkpoints
- Tones match urgency
Example:
Agent: "You asked to follow up on the client doc today; want me to send it now?"
Fit Criteria:
- Task is trackable or deadline-based
- User forgetfulness is common
Dependencies:
- Time awareness or calendar sync
- Lightweight task state
Anti-Patterns:
- Repeated reminders with no adjustment
- Tracking unimportant or trivial tasks
Pattern 5: Handoff + Resume
Definition:
Agent pauses a flow, saves the state, and picks it up later; either by user signal or contextual detection.
Best Used When:
- Tasks span multiple sessions
- Users switch context often
Behavior:
- Flow can pause cleanly
- Resumes with minimal recap
Example:
Agent: "You left off reviewing the proposal draft. Pick up where you left off?"
Fit Criteria:
- Flows are interruptible
- Resume point is recoverable
Dependencies:
- Session memory or state store
- Re-entry logic with fallback
Anti-Patterns:
- Losing state
- Restarting from scratch unnecessarily
Pattern 6: Multi-Intent Fork
Definition:
Agent detects multiple possible user intents and presents clarifying options.
Best Used When:
- Input is ambiguous
- User could be pursuing more than one path
Behavior:
- Intercepts and offers forks
- Clarification feels natural
Example:
User: "Schedule something with Jordan."
Agent: "Meeting, call, or follow-up task?"
Fit Criteria:
- Ambiguity is common
- Options are clear and unambiguous
Dependencies:
- Intent parser with ranking
- Option presentation UI
Anti-Patterns:
- Asking user to repeat themselves
- Offering irrelevant or confusing forks