Context Layer
Managing what the agent knows and remembers
Managing what the agent knows and remembers
Agents with memory feel intelligent. Agents without memory feel broken. Context builds relationship, relevance, and continuity.
Agentic Use Case Examples
Category
Retail, eCommerce & Consumer Goods
Example
Shopping agent learns and reuses preferences to tailor each session.
Industry
Real Estate & Construction
Example
Building agent uses environmental context and past user patterns to adapt systems.
Industry
Customer Experience & Lifecycle
Example
CX agent adjusts interactions using journey history and channel context.
Industry
Sustainability, ESG & Climate
Example
ESG agent remembers progress, adapts plans, and contextualizes impact data.
Applying the Context Layer in Product Development
1
Solves for: No shared understanding of what the agent should remember.
Solves for: Context handling is treated as a backend detail, not a UX concern.
Solves for: Personalization is an afterthought, not a core capability.
Project Kickoffs
1
Solves for: No shared understanding of what the agent should remember.
Solves for: Context handling is treated as a backend detail, not a UX concern.
Solves for: Personalization is an afterthought, not a core capability.
Project Kickoffs
1
Solves for: No shared understanding of what the agent should remember.
Solves for: Context handling is treated as a backend detail, not a UX concern.
Solves for: Personalization is an afterthought, not a core capability.
Project Kickoffs
2
Solves for: Agent forgets prior interactions or user preferences.
Solves for: System behavior feels out of sync with user goals or environment.
Solves for: Memory and adaptation are invisible or untrustworthy.
Audits
2
Solves for: Agent forgets prior interactions or user preferences.
Solves for: System behavior feels out of sync with user goals or environment.
Solves for: Memory and adaptation are invisible or untrustworthy.
Audits
2
Solves for: Agent forgets prior interactions or user preferences.
Solves for: System behavior feels out of sync with user goals or environment.
Solves for: Memory and adaptation are invisible or untrustworthy.
Audits
3
Solves for: New capabilities assume context without surfacing it to users.
Solves for: Agent recalls information users didn’t expect it to retain.
Solves for: Features create memory debt without transparency or control.
Feature Reviews
3
Solves for: New capabilities assume context without surfacing it to users.
Solves for: Agent recalls information users didn’t expect it to retain.
Solves for: Features create memory debt without transparency or control.
Feature Reviews
3
Solves for: New capabilities assume context without surfacing it to users.
Solves for: Agent recalls information users didn’t expect it to retain.
Solves for: Features create memory debt without transparency or control.
Feature Reviews
4
Solves for: No probes to test contextual continuity or personalization.
Solves for: Users report starting from scratch with every session.
Solves for: Participants mistrust agents that behave inconsistently across time.
User Research
4
Solves for: No probes to test contextual continuity or personalization.
Solves for: Users report starting from scratch with every session.
Solves for: Participants mistrust agents that behave inconsistently across time.
User Research
4
Solves for: No probes to test contextual continuity or personalization.
Solves for: Users report starting from scratch with every session.
Solves for: Participants mistrust agents that behave inconsistently across time.
User Research
Real-World Use of the Context Layer
Claude 2 from Anthropic uses a massive 100K-token context window to retain long-form conversation history or process complex documents. This enables the AI to maintain coherence over time, refer back to earlier points, and personalize its responses, offering a memory-augmented experience that mirrors real human conversation.
Claude 2 from Anthropic uses a massive 100K-token context window to retain long-form conversation history or process complex documents. This enables the AI to maintain coherence over time, refer back to earlier points, and personalize its responses, offering a memory-augmented experience that mirrors real human conversation.
Claude 2 from Anthropic uses a massive 100K-token context window to retain long-form conversation history or process complex documents. This enables the AI to maintain coherence over time, refer back to earlier points, and personalize its responses, offering a memory-augmented experience that mirrors real human conversation.
Microsoft’s Business Chat accesses Outlook, Teams, and calendar data to synthesize context-aware answers enabling real-time, unified, and personalized assistance.
Microsoft’s Business Chat accesses Outlook, Teams, and calendar data to synthesize context-aware answers enabling real-time, unified, and personalized assistance.
Microsoft’s Business Chat accesses Outlook, Teams, and calendar data to synthesize context-aware answers enabling real-time, unified, and personalized assistance.
Intuit Assist autonomously performs financial tasks (e.g. forecasting, cash flow optimization), but defers final decisions to users—balancing AI autonomy with human oversight.
Intuit Assist autonomously performs financial tasks (e.g. forecasting, cash flow optimization), but defers final decisions to users—balancing AI autonomy with human oversight.
Intuit Assist autonomously performs financial tasks (e.g. forecasting, cash flow optimization), but defers final decisions to users—balancing AI autonomy with human oversight.
Bing Chat pulls in real-time web content and search results to enhance its responses, grounding outputs in the latest context beyond model training, useful for dynamic topics like news or prices.
Bing Chat pulls in real-time web content and search results to enhance its responses, grounding outputs in the latest context beyond model training, useful for dynamic topics like news or prices.
Bing Chat pulls in real-time web content and search results to enhance its responses, grounding outputs in the latest context beyond model training, useful for dynamic topics like news or prices.
Rewind AI captures and indexes all on-device activity like calls, browser sessions, meetings, so users can query the AI for exact details from prior experiences, powering hyper-personalized context recall.
Rewind AI captures and indexes all on-device activity like calls, browser sessions, meetings, so users can query the AI for exact details from prior experiences, powering hyper-personalized context recall.
Rewind AI captures and indexes all on-device activity like calls, browser sessions, meetings, so users can query the AI for exact details from prior experiences, powering hyper-personalized context recall.