Summary

This article examines how Chiho uses AI Agents as operational units. Unlike prompt-based usage, which produces variable results, Agents are defined by role, scope, and task logic. They collaborate within workflows, maintain consistency through validation and logging, and convert department knowledge into repeatable output.

1. Why Companies Need AI Specialists, Not AI Chatbots

Most organizations experiment with AI through chat interfaces, writing prompts, requesting summaries, or generating drafts. This approach has clear limitations:

• Inconsistent results Different users issue different prompts. Even with templates, output varies depending on phrasing, model, and context.
• Lack of operational structure Chatbots don’t know:
  • who owns a task
  • what a “good” output looks like
  • how today’s result should relate to last week’s
  • what steps must occur before or after the request
• No accountability or traceability Chats rarely store logs in a form suitable for team-wide review or debugging.

Why specialization matters

To ensure reliability, AI needs boundaries:

• “What is this task?”
• “What knowledge should be referenced?”
• “What standards define a correct output?”

Chiho’s Agents address these gaps by acting as role-based virtual specialists, rather than general chat responders.

2. How Agents Are Built

An AI Agent in Chiho is defined through several components. These components constrain its behavior, producing consistent output even when different users trigger the same process.

(1) Role Definition

Each Agent has a clear function, e.g.,

• meeting summarizer
• task extractor
• requirement analyzer
• report formatter
• notification preparer

This anchors the model in an operational identity, similar to a team member with a single responsibility.

(2) Knowledge Scope

Although Chiho no longer positions Spec Hub as a feature name, Agents still rely on structured internal information. Their scope includes:

• relevant documents
• recent discussions
• historical outputs
• system data retrieved via tools

This prevents Agents from referencing unrelated information and improves consistency.

(3) Prompt Architecture

Each Agent embeds several layers of instruction:

• role-specific instructions
• task logic (steps expected in the output)
• format rules
• validation criteria
• edge-case handling

This replaces ad-hoc prompting with a stable operational specification.

(4) Model Selection Logic

Workflows can decide which model an Agent uses for each step:

• GPT for structured reasoning
• Claude for longer-context analysis
• Gemini for certain text generation cases

This ensures the Agent performs its task with the model best suited for that stage.

3. How Agents Collaborate

Most real processes require multiple specialists, not a single generalist. Chiho models this through agent-to-agent collaboration inside workflows.

Passing Outputs

The output of one Agent becomes the input for the next. For example:

• Agent A → meeting summary
• Agent B → task extraction
• Agent C → Slack-ready notification

Each step transforms data without requiring humans to reformat or reinterpret.

Task Ownership

Each Agent is responsible for one stage only. This prevents:

• prompt drift
• inter-step inconsistencies
• confusion about responsibility

Maintaining Consistency

Because Agents are defined with fixed logic, the workflow produces:

• the same structure
• the same explanation standards
• predictable variation controls

Example: Weekly Reporting Workflow

A typical multi-agent flow may run like this:

• Agent A reads discussion data → produces structured meeting summary
• Agent B extracts tasks with owners and deadlines
• Agent C prepares outbound notifications for Slack
• Tool layer posts results automatically

The same request, even from different users, yields the same structure each time.

4. Reliability Measures

Chiho integrates several mechanisms to ensure Agents behave predictably and remain aligned with internal expectations.

(1) Input Validation

Before generating output, Agents verify:

• whether required data exists
• whether the format is valid
• whether key fields are missing

Invalid inputs trigger corrective prompts or fallback behavior.

(2) Logs

Every step records:

• prompts
• model versions
• input references
• output results
• errors

This is essential for debugging and for understanding how workflows behave over time.

(3) Comparison With Historical Outputs

Agents can reference previous outputs, ensuring continuity in:

• terminology
• structure
• recurring project details

This avoids sudden shifts in style or interpretation.

(4) Hallucination Prevention

Through role constraints, validation rules, and controlled tool access, Chiho minimizes the likelihood of producing unsupported claims or speculative content. Agents do not improvise; they operate within defined task boundaries.

Conclusion

AI Agents represent a structured way to operationalize expertise. Instead of depending on prompt-writing or individual user habits, Chiho defines each Agent through role, knowledge scope, and logic. When combined in workflows, they produce stable, repeatable output with transparent logs and predictable behavior. This approach makes automation viable for processes that require both consistency and explainability, something traditional chatbot-based usage cannot provide.