Bot

The sustained mediation between actor intent and system capability, transforming fuzzy goals into concrete outcomes through an adaptable intermediary that evolves with use. This umbrella term encompasses all user-facing AI interactions — from chatbots to agentic systems and beyond.

Agency

Bot patterns embody different levels of system agency from the agency spectrum:

1. Chatbot: User-driven interaction requiring explicit prompts
2. Inline: Assistance mode providing contextual help and smart suggestions
3. Workflow automation: autonomous action that follows predefined sequence
4. Full autonomy: proactive monitoring and acting without direct user input

Higher agency modes require more careful preservation of user agency to maintain control and transparency.

Modes of interaction

• Focused: Bot as a primary interface, typically via messaging.
• Inline: Contextual interactions embedded directly in the UI, such as AI completion, rewrite suggestions, or smart defaults.
• Ambient: Behind-the-scenes monitoring or support, offering proactive help or taking actions without direct prompting. Next-best action is the primary pattern here.

Variants

Single-turn interactions

Simple query-response patterns without state persistence or tool use. Classic chatbots often operate in this mode.

Multi-turn conversations

Maintaining context across exchanges, allowing for clarification, follow-ups, and evolving understanding.

Tool-augmented agents

Bots that can invoke external capabilities—from simple calculations to complex API calls. This includes the bounded loop execution pattern where agents run tools iteratively until reaching objectives.

Memory

Short-term

Conversation context provides implicit short-term memory, allowing bots to maintain state across interactions within a session.

Long-term

Persistent memory requires explicit implementation through:

• Storage tools (databases, files, vector stores)
• Retrieval mechanisms with semantic search
• Memory management strategies to prevent unbounded growth

Anti-patterns

Algorithmic loafing

When bots take creative or cognitive lead, humans reduce effort and disengage—accepting bot output with minimal reflection. This manifests as:

• Reduced cognitive engagement despite task importance
• Loss of skill development and expertise over time
• Decreased perceived ownership and commitment to outcomes
• Homogenisation of outputs across users

The effect parallels social loafing, where individuals reduce contribution when responsibility diffuses.

Mitigation: Design interactions where humans remain in creative or cognitive lead whilst bot provides scaffolding through questions, analogies, or contextual knowledge rather than complete solutions. See Conversation dialogue forms for question-driven approaches that maintain engagement.

Mismatched scope

Applying bots to tasks requiring genuine accountability, sustained comprehension, or human empathy. Bots cannot take responsibility for actions or form independent intentions, and their understanding operates within learned patterns without true comprehension.

Uncalibrated trust

Treating bot outputs as authoritative without verification mechanisms. Bots can generate plausible but incorrect information, and performance degrades with extended interactions or complex state.

Undefined boundaries

Failing to communicate bot capabilities and limitations, leading to misuse and frustration. Users need clear understanding of what bots can and cannot do to set appropriate expectations.

Related components

• Bot can use messaging to converse with actors.

Resources & references

• Maier, Schneider, Feuerriegel (2025) Partnering with Generative AI: Experimental evaluation of human-led and model-led interaction in human-AI co-creation
• Nardi, B., Miller, J. R., & Wright, D. J. (1998) Collaborative, programmable intelligent agents. Communications of the ACM, 41(3), 96–104.
• Thu, Kocaballi (2025) Personalization features for Human-LLM Interactions
• Simon Willison (2025) Agents
• Amelia Wattenberger (2024) Why chatbots are not the future
• Luke Wroblewski (2023) Expanding conversational user interfaces
• Andy Matuschak (2025) How might we learn? – guidance in action and the critique of transactional chatbot tutoring