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Feature Specification: Interactive Agent Testing

Feature Branch: 007-interactive-chat Created: 2025-11-22 Status: Shipped Input: User Story 4 - Interactive Agent Testing (Priority: P2)

User Scenarios & Testing (mandatory)

User Story 1 - Basic Chat Session (Priority: P1)

A developer initializes an agent using their existing YAML configuration and starts an interactive chat session to test agent behavior in real-time. They want to send messages and receive responses with visible tool execution happening transparently.

Why this priority: Essential foundation for the chat feature. Without this, developers cannot test their agents interactively. This is the core MVP for the chat command.

Independent Test: Can be fully tested by running holodeck chat agent.yaml, sending a simple message, and verifying the agent responds. Developers can validate their agent setup works in interactive mode.

Acceptance Scenarios:

  1. Given an initialized agent with valid YAML configuration, When the user runs holodeck chat agent.yaml, Then an interactive terminal prompt appears ready for user input
  2. Given an active chat session, When the user sends a message, Then the agent processes the message and returns a response within 5 seconds (excluding LLM provider latency)
  3. Given a chat response from the agent, When the agent uses tools, Then the tool calls are visibly logged or displayed to show execution
  4. Given an active chat session, When the user types 'exit' or 'quit', Then the session terminates gracefully with a farewell message

User Story 2 - Multi-turn Conversation Context (Priority: P2)

A developer wants to have multi-turn conversations with their agent where context is preserved across messages. The agent should remember previous exchanges and respond based on the full conversation history.

Why this priority: Enables more realistic testing scenarios. Single exchanges are insufficient for testing agents designed to handle complex, multi-step interactions. Important for developer experience but requires conversation state management.

Independent Test: Can be tested by sending 3-5 sequential messages to the agent and verifying the agent's responses reference or build upon earlier messages in the conversation.

Acceptance Scenarios:

  1. Given an active chat session with prior message exchanges, When the user sends a follow-up message, Then the agent's response demonstrates awareness of previous context
  2. Given a multi-turn conversation, When the user asks the agent to recall earlier information, Then the agent correctly references the prior exchange

User Story 3 - Session Persistence and Recovery (Priority: P3)

A developer wants to save their chat session and resume it later, preserving the conversation history and context. This allows them to continue debugging or testing without losing progress.

Why this priority: Enhances developer workflow for long debugging sessions. Not critical for MVP but valuable for advanced use cases. Can be implemented after basic chat is stable.

Independent Test: Can be tested by saving a session, exiting the chat, and then resuming the same session to verify conversation history is preserved and the agent can reference prior exchanges.

Acceptance Scenarios:

  1. Given an active chat session, When the user types 'save', Then the session is saved with a timestamp and session ID
  2. Given a previously saved session, When the user runs holodeck chat agent.yaml --resume <session-id>, Then the conversation history is restored and the agent can reference prior exchanges

User Story 4 - Error Handling and Invalid Inputs (Priority: P2)

A developer sends invalid inputs, malformed requests, or attempts to access unavailable tools. The chat interface handles these gracefully without crashing.

Why this priority: Critical for reliability and user experience. Developers expect clear error messages rather than cryptic failures. Prevents frustration and supports debugging.

Independent Test: Can be tested by sending various invalid inputs (empty messages, very long messages, special characters) and verifying the system handles each gracefully with informative feedback.

Acceptance Scenarios:

  1. Given a chat session, When the user sends an empty message, Then the system requests valid input without crashing
  2. Given a chat session, When an agent tool fails or is unavailable, Then the error is clearly communicated to the user with context
  3. Given a chat session, When the underlying agent configuration is invalid, Then a helpful error message is displayed before the session starts

Edge Cases

  • What happens when the user sends extremely long messages or many messages in rapid succession?
  • How does the system handle if an agent tool takes an unusually long time to execute?
  • What happens if a developer provides an invalid agent.yaml file path to the chat command?
  • How should the chat handle if the LLM provider is temporarily unavailable or returns an error?
  • What if the agent has conflicting or circular tool dependencies?
  • How does the system behave if conversation history grows very large (memory/performance concerns)?

Requirements (mandatory)

Functional Requirements

  • FR-001: System MUST load and validate agent configuration from a provided YAML file
  • FR-002: System MUST establish a real-time interactive chat session that accepts user input via terminal
  • FR-003: System MUST send user messages to the agent for processing and retrieve responses
  • FR-004: System MUST maintain conversation history within a single chat session
  • FR-005: System MUST display agent responses to the user in a clear, readable format
  • FR-006: System MUST stream tool execution events in real-time within the conversation showing: tool name, parameter summary, execution time, and result status (standard verbosity)
  • FR-016: System MUST support --verbose/-v flag to display full tool execution details including raw parameters and internal state for advanced debugging
  • FR-007: System MUST gracefully terminate the chat session when user enters exit/quit commands
  • FR-008: System MUST provide informative error messages when agent configuration is invalid
  • FR-009: System MUST handle cases where the agent tool execution fails or times out
  • FR-010: System MUST validate user input by: rejecting empty/whitespace-only messages, enforcing reasonable size limits (~10K characters), stripping control characters, and validating UTF-8 encoding
  • FR-014: System MUST escape and sanitize tool outputs before displaying them in the terminal to prevent terminal corruption or injection attacks
  • FR-015: System MUST provide extensible validation architecture to support future safety filters (e.g., prompt injection detection, content filtering) without code changes
  • FR-011: System MUST support agent configurations with multiple tool types (function, MCP, vector search, prompt-based)
  • FR-012: System MUST pass conversation context to the agent for multi-turn interactions
  • FR-013: System MUST warn developers when conversation history approaches a reasonable limit (e.g., 50 messages or 80% of model context) and offer options to save the session or clear history

Key Entities

  • Chat Session: Represents an active conversation between developer and agent. Contains conversation history (list of messages), session metadata (start time, agent configuration), and state management.
  • Message: Represents a single exchange in the conversation. Contains role (user/agent), content, timestamp, and optional metadata (tool calls made, execution time).
  • Agent Configuration: Loaded from YAML and defines the agent's behavior, tools, LLM settings, and system instructions.
  • Tool Execution: Metadata about tool calls made during agent processing, including tool name, parameters, result, and execution time.

Success Criteria (mandatory)

Measurable Outcomes

  • SC-001: Developers can start an interactive chat session within 1 second of running holodeck chat agent.yaml
  • SC-002: Agent responses are received and displayed within 5 seconds of user sending a message (not including LLM latency)
  • SC-003: Multi-turn conversations with 10+ message exchanges work without performance degradation
  • SC-004: 95% of invalid input scenarios are handled gracefully with clear error messages
  • SC-005: Chat can run for at least 30 minutes without memory leaks or performance degradation
  • SC-006: Tool execution events are visible to the user within the chat interface
  • SC-007: Developers successfully debug and test agent behavior using interactive chat (qualitative: validated through user testing)

Assumptions

  • The Agent Engine is available or can be implemented in parallel to load and execute agents
  • LLM provider (OpenAI, Anthropic, etc.) is configured and accessible during testing
  • Users have valid YAML agent configuration files on their system
  • Interactive terminal input/output is available in the developer's environment
  • Conversation history for MVP is stored in-memory only; persistent storage (P3) requires explicit user action to save
  • Session persistence (P3) can be handled as a follow-up feature; basic sessions do not require saving
  • Tool execution logging can leverage existing system logging infrastructure

Out of Scope

  • Web-based chat interface (terminal-only for MVP)
  • Multi-agent conversations
  • Chat history export in multiple formats
  • Advanced conversation analysis or metrics
  • Chat interface customization or theming

Clarifications

Session 2025-11-22

  • Q1: How should tool execution (calls, progress, results) appear in the chat interface? → A: Inline streaming - Tool calls and results appear in real-time as they execute, flowing into the conversation naturally.
  • Q2: What level of input validation and sanitization should be applied? → A: Standard safety (input size limits ~10K chars, control char stripping, UTF-8 validation; output escaping for terminal display) with extensible architecture for future safety filters (prompt injection detection, content filters, etc.).
  • Q3: Where should conversation history be stored during a session? → A: In-memory only for MVP. History is lost on session exit. File-based persistence deferred to User Story 3 (P3 feature).
  • Q4: What level of detail should tool execution logs show? → A: Standard verbosity (tool name, param summary, execution time, result status) with optional --verbose/-v flag for detailed debugging including raw parameters and internal state.
  • Q5: Should "reasonable time" in acceptance scenarios be aligned to specific targets? → A: Yes, align acceptance scenario to 5-second target from SC-002 for consistency and testability.