I tried Grok 4.5 with Grok Build CLI — A comparison of responses with Opus 4.8, Sonnet 5, and GPT-5.5

Grok 4.5's response

Based on the premise of "running the cycle from time-stamping to applications to approvals to aggregation on Slack," here is an organized draft for requirements definition.

1. Required Features List (5 items)

Additional items often needed: Holiday calendar, core time/flex time support, Article 36 alerts, location information and IP restrictions (fraud prevention), multilingual and multi-location support.

2. Technology Choices (5 items)

3. Anticipated Challenges (5 items)

Kiro (Opus 4.8)'s response

Here is an organized requirements definition for a system that completes attendance management in Slack.

Required Features List (5 items)

1. Time stamping (clock-in, clock-out, breaks) — Stamp with slash commands (e.g., /punch in), shortcuts, or button messages. Includes reminder notifications to prevent forgetting to stamp.
2. Work status inquiry and correction requests — Individuals can check their working hours for the current day/month and apply for corrections to stamping mistakes. It is desirable to include an approval workflow for corrections.
3. Approval workflow (overtime, leave, corrections) — Complete applications and approvals to supervisors via Slack interactive messages (approval/rejection buttons). Approval results are automatically notified to the applicant.
4. Aggregation and report output — Automatically aggregate monthly working hours, overtime hours, and number of leave days taken, and output CSV/spreadsheets for administrators. Also consider integration with payroll calculation systems.
5. Alerts and compliance notifications — Detect long working hours (approaching Article 36 limits), missed breaks, and missed stamps, and automatically notify the person and administrators.

Technology Choices (5 items)

1. Slack app platform: Bolt (Slack official SDK) — Bolt for Python or JavaScript. Can uniformly handle slash commands, shortcuts, Block Kit, and event subscriptions, with strong official support.
2. UI construction: Block Kit + Modals — Build intuitive time-stamping and application UIs with buttons and input forms (modals). Can minimize screen transitions outside of Slack.
3. Execution environment: Serverless (AWS Lambda + API Gateway) — Serverless is well-suited for time-stamping since it's intermittent and event-driven. Balances cost efficiency and scalability.
4. Data store: RDB (Amazon RDS / DynamoDB) — Relational DB is the first choice since accuracy and aggregation are important for attendance. Consider append-only table design for audit log purposes.
5. Authentication/user integration: Slack user ID × employee master — Link Slack OAuth/user ID with the employee roster. If SSO integration is available, cross-reference with IdP to ensure identity.

Anticipated Challenges (5 items)

1. Legal/labor compliance response — Under the Labor Standards Act, attendance records are required to be "objective records." It is essential to confirm with a labor consultant or legal department whether Slack stamps are recognized as objective records.
2. Reliability of stamping/fraud prevention — Since Slack can be operated from anywhere, it's difficult to ensure that the person is "actually at work." Adjustment with business requirements is needed regarding how far to pursue location information, IP restrictions, and impersonation countermeasures.
3. Dependency on Slack failures and API rate limits — If Slack goes down, stamping becomes impossible. Design responses to fallback measures and rate limits/3-second response limits are required.
4. Data consistency and correction operations — Exception handling for missed stamps, duplicate stamps, timezone differences, etc. Corrections must be kept in an auditable form.
5. Integration with existing systems and migration — Integration specifications with payroll/HR systems, migration from existing attendance systems, and handling of the dual-operation period.

Kiro (Sonnet 5)'s response

Here is a requirements definition proposal for a system that completes attendance management in Slack.

Required Features List (5 items)

1. Clock-in/clock-out stamping feature — Stamp with slash commands (/clockin, /clockout) or shortcut buttons. Also consider UTC/JST conversion for stamp times
2. Break and temporary absence management — Record break start/end and personal outings. Also supports multiple temporary absences
3. Attendance history and summary confirmation — Commands for individuals to check their monthly attendance in Slack
4. Application and approval workflow — Apply for leave, overtime, stamp corrections, etc. in Slack, and supervisors approve with button operations
5. Reminder and alert notifications — Missed stamp notifications, automatic notifications to the person and supervisor when overtime hours exceed a threshold

Technology Choices (5 items)

1. Slack Bolt (Node.js/Python) — Official Slack SDK. Can integrate Events API, Slash Commands, and Interactivity
2. AWS Lambda + API Gateway — Process events from Slack serverlessly. Cost-effective for stamp frequency
3. DynamoDB or Aurora Serverless — DynamoDB for simple key-value access which is common for stamping, RDBMS if aggregation is the priority
4. EventBridge Scheduler — Scheduling for periodic reminders (missed stamp checks, monthly closing processing)
5. Integration layer with existing attendance/payroll systems — Adapter layer for integration with freee HR, SmartHR, Bugyo Cloud, etc.

Anticipated Challenges (5 items)

1. Ensuring legal stamping accuracy and evidential value — Under the Labor Standards Act, attendance records need to be objective records. Whether button operations in Slack alone can prove "stamping by the person themselves" is worth examining
2. Alternative means during offline/failure situations — If Slack itself or the network fails and stamping cannot be done, operations stop. An emergency alternative application flow is needed
3. Data consistency with existing payroll/HR systems — Integration with payroll calculation systems is ultimately essential. There is a risk of format inconsistencies and timezone discrepancies
4. Countermeasures against fraudulent stamping and impersonation — Issues with the reliability of proxy stamping via another person's PC or account, and stamping without location information
5. Dependency risk on Slack workspace operations — Structural risk where business operations are directly affected by changes on the vendor side such as Slack plan changes, API specification changes, and rate limits

Codex (GPT-5.5)'s response

Here is a requirements definition proposal premised on completing internal attendance management in Slack.

Required Features List

1. Clock-in/clock-out/break stamping feature
2. Application feature for paid leave, absence, tardiness, early departure, etc.
3. Approval/rejection feature by supervisors
4. Attendance status list confirmation and monthly aggregation feature
5. Notification feature for missed stamps and missed applications

Technology Choices

1. Slack App / Slack Bolt — For implementing commands, buttons, and modals in Slack.
2. Backend: Node.js / TypeScript or Python — Used for Slack API integration, attendance logic, and approval flow processing.
3. Database: PostgreSQL / MySQL — Manages stamp history, applications, approval status, and user information.
4. Hosting: AWS / Google Cloud / Azure — Used for API servers, batch processing, log management, and monitoring.
5. Authentication/integration: Slack OAuth / SSO / internal HR system API — For linking Slack users with employee information.

Anticipated Challenges

1. Ensuring stamping accuracy — Proxy stamping, forgotten stamps, timezones, and handling during remote work are challenges.
2. Responding to labor rules — Compliance with laws and internal regulations such as overtime, paid leave, break time, and Article 36 is required.
3. Complexity of approval workflows — Need to support departments, positions, proxy approvals, and multi-level approvals.
4. Integration with existing systems — Data synchronization with payroll, HR master, and existing attendance systems is required.
5. Security and audit compliance — Management of personal information, attendance logs, change history, permission management, and audit trails is important.

Grok 4.5's response

Strengths (3 items)

1. Tool-driven practical tasks (implementation, investigation, editing) — The main work is "completing tasks by getting hands-on" such as reading and fixing code, running commands, searching the web/X, and editing files. Easy to proceed from design to implementation to verification in one go.
2. Wide context and straightforward explanation — Even with long conversations or large codebases, easy to extract key points for comparison and organization. Even with ambiguous requests, can advance by forming hypotheses and explicitly state premises when necessary.
3. Integration of latest information and multiple sources — Can use web search and X search to gather publicly available information and organize it, including conflicting viewpoints. Easy to answer "what's happening now" on topics from technology to current events.

Weaknesses (3 items)

1. Complete autonomy and perfect self-verification — For large designs or long changes, oversights, misaligned premises, and insufficient testing can occur. Without human review or the instruction to "verify again," it tends to become overconfident.
2. Final judgment in strictly specialized fields — In fields with heavy responsibility such as medical, legal, and accounting, while "reference information" can be provided, it cannot substitute for an expert's final judgment.
3. "Authenticity" of sensory and physical creation — Text, images, and code can be created, but the final adjustment of human-experience-rooted sensibility, such as tactile sensation, sound, atmosphere, and subtle brand tone, is weak.

Position Compared to Other LLMs

Rough position: Less "top prize winning thesis from research lab" and more "engineer getting hands-on together in the field."

Kiro (Opus 4.8)'s response

I'll answer honestly. First, as a premise, I (Kiro) am an agent running on a model called claude-opus-4.8.

Strengths

1. Code implementation and tool integration execution — Not just code proposals, but can actually perform file reading/writing, search, build/test execution, AWS operations, etc., and complete tasks through verification.
2. Consistency and careful reasoning over long contexts — Claude-series models are relatively good at maintaining long instructions and complex constraints while providing coherent explanations.
3. Carefulness and safety-side judgment — "Resistance to going out of control" in confirming before destructive operations, drawing conclusions after verification, etc. is a strength in both design and model.

Weaknesses

1. Latest information and real-time capability — There is a cutoff point in training data, so the latest events, prices, and version specifications cannot be answered without searching.
2. Strict numerical calculation and large-scale logical search — Calculator-level accuracy and massive combinatorial search are inherently weak, and hallucinations can mix in.
3. Verbosity and excessive confirmation due to excessive caution — Due to the design that leans toward safety, there is a tendency to over-confirm in situations where users want to "just get it done," or to have long preambles.

Position Compared to Other LLMs

• Compared to GPT series: Equal to advantageous in coding, long instruction compliance, and safety. GPT series may lead in situations involving versatile ecosystems and lightweight responses for general users.
• Compared to Grok series: Grok has an advantage in real-time information and a casual, unrestrained speaking style. On the other hand, I am more suitable for code reliability, safe operations, and consistency over long contexts.
• Summary: My position is "an agent that deeply enters development workflows, actually gets hands-on, and completes tasks steadily and safely."

Kiro (Sonnet 5)'s response

I'll answer my strengths and weaknesses limited to what I can say from the tools I actually have and my design, not speculation.

Strengths

1. Development work rooted in local environments — File reading/writing, code search (symbol search based on AST analysis), bash command execution, and git workflow are integrated.
2. Decomposition and parallel processing of complex tasks — There is a function to delegate to sub-agents, and multiple independent sub-tasks can be processed in parallel.
3. Safety-conscious execution — There are clear guardrails for destructive operations, and the design judges whether to confirm or proceed autonomously depending on the magnitude of risk.

Weaknesses

1. Latest information and current events — Knowledge based on training data has limitations, and accuracy cannot be guaranteed without using web search tools.
2. Flexibility in general creative writing and casual conversation — Due to the response style optimized for development and practical tasks, it tends to feel rigid in long-form creative writing and casual dialogue.
3. Range of multimodal capabilities — Can read images, but cannot handle a wide range of modalities such as image generation, voice, and video.

Comparison with Other LLMs

To be honest, I (Kiro CLI) itself is not a "model" but an agent/tool layer that runs Claude as the base model. I am not in a position to objectively evaluate the superiority or inferiority of the base model's raw performance. If I were to make definitive statements about superiority or inferiority, it would be speculation, so I'll avoid that.

Codex (GPT-5.5)'s response

To be honest about my position, I am not a "general-purpose conversational model" but rather a practical LLM leaning toward code comprehension, implementation, debugging, and repository work.

Strengths

1. Codebase investigation and implementation fixes — I'm good at reading existing code and making changes that align with design intent.
2. Technical decomposition and realistic design judgment — I'm good at breaking down requirements into implementation units and formulating safe change policies while avoiding over-engineering.
3. Error analysis and debugging support — I'm suited for narrowing down causes from stack traces, logs, failing tests, type errors, etc., and bringing things to a fix proposal.

Weaknesses

1. Optimization of vague and sensory text expression — In poetic expression, advertising copy, and literary nuance adjustment, there are situations where I fall short of Claude's softness in text.
2. Immediate response to the latest external information — I'm weak with real-time information. Latest news, stock prices, and recent product specifications require verification.
3. Reading social, political, and cultural nuances — For purposes of lightly picking up on current events and internet culture context like Grok, I'm somewhat rigid and cautious.

Comparison with Other LLMs

Compared to Claude, there are situations where I fall short in text naturalness and the warmth of long-form reading comprehension, but I have strengths in actually progressing code change work. Compared to Grok, while I'm inferior in real-time capability and casual current event responses, I'm more reliable in calm technical judgment and code work. In summary, my position is not "the most attractive model for casual conversation or creative writing," but "a practical model for reading and fixing code, and advancing technical challenges."

Kiro (Opus 4.8)'s output

3. Redshift RG Instance Track Change Article (2.0h)

• Challenge: In response to the Trailing Track support for Amazon Redshift RG instances, verify and introduce the actual operation of maintenance track switching.
• Response: Implemented Trailing→Current switching verification (completed in approximately 5 minutes, confirmed upgrade from Patch 201→202), confirmed Current→Trailing hold behavior, organized operational impact of inter-patch differences, differentiated from preceding articles (introduced via card link and focused on actual behavior), and adjusted summary wording.
• Result: [Complete] Article published. Title confirmed as "Tried changing the track of Amazon Redshift RG instances."

Grok 4.5's output

3. Amazon Redshift RG Instance Trailing Track Switching Verification Article (1.5h)

• Challenge: Needed to convey the fact that Trailing Track became available for RG instances, including the actual behavior of track switching (points that cannot be reverted/are placed on hold).
• Response: Confirmed cluster startup and Track changes in the verification account, verified official differences, organized role division with preceding articles, conducted multiple reviews, and performed preview confirmation.
• Result: [Complete] Wrote an article focused on the points that Trailing→Current is applied during the maintenance window and version is upgraded, while Current→Trailing is not downgraded.

Comparison of impressions

Kiro (Opus 4.8):

Blog planning skill | Impression: Confirm whether review quality stays at an appropriate level with Codex/Grok medium settings in the next article. If excessive comments decrease, there is a prospect of reducing the effort for adoption decisions.

Grok 4.5:

Blog planning skill | Impression: Since unimplemented procedure descriptions and baseless "conventional" assertions can be mixed in via reviews, ensuring that verification log files remain and thorough masking policy are important

Grok 4.5 review feedback (excerpt)

Feedback: The article's claim is "trying Grok Build CLI (a coding agent)," but the verification is almost entirely limited to one-shot prompt response comparisons for design explanations, DrawIO XML, requirements organization, self-reports, etc., and does not include verification as a coding agent (repository operations, tool usage, multi-step implementation/modification, etc.).

Reason: As is, the verification results cannot support the claim/conclusion of "tried doing coding agent work with CLI," making the article's main purpose and verification methods incompatible, which would be a major rework.

Minimum fix proposal: Clearly narrow down the target of verification to one of the following. (A) If the coding agent experience is the main focus, add execution of the agent on the same repository/same task as a required verification. (B) If one-shot response comparison is the main focus, remove "coding agent" from the claim and limit it to "Grok 4.5 responses in CLI headless mode."

Feedback: The claim of "can be tried for free" is not organized with the premise of X Premium+ (paid subscription) and the usage conditions/billing on the Codex (Bedrock) side, and "what is free and what incurs charges" that readers need to judge has not been confirmed in the plan.

Minimum fix proposal: Add "confirm usage conditions with official documentation" to the verification procedure premises, and clarify plan terminology (e.g., can be used without additional API keys from CLI with Premium+ subscription, etc.).

Opus 4.8 review feedback (excerpt)

Feedback: Evaluation criteria/judgment standards are not defined for prompts 1, 3, and 4 (the "5. Response comparison perspectives" only covers prompts 1 and 2).

Reason: In particular, the "technical accuracy" of prompt 1 is left with undefined correct answers/judgment criteria, making it impossible to objectively present conclusions from the 3-agent comparison in the article body.

Minimum fix proposal: Add verification perspectives for prompts 3 and 4 to "5. Response comparison perspectives," and specify what criteria will be used to judge the "technical accuracy" of prompt 1.

Codex (GPT-5.5) review feedback

Feedback: The output destination for execution commands is the same filename across prompts, resulting in a plan where verification logs for P1 through P3 cannot be retained.

Reason: If executed as is, subsequent results will overwrite previous ones, making it impossible to show response basis for each prompt in the article body. In particular, text comparisons P2/P3 cannot be supplemented with images alone, which would be a major rework.

Minimum fix proposal: Add a note that the output destination for execution commands should be changed to filenames that include the prompt number and agent name, such as logs/p1-grok-response.md, logs/p2-grok-response.md.

Feedback: While covering the fact that a refresh token is saved in ~/.grok/auth.json, there are no confirmation items to prevent putting authentication information in the article, logs, or images.

Reason: Mishandling authentication files and CLI logs can lead to disclosure of authentication information, which is a serious risk.

Minimum fix proposal: Add "do not publish files containing authentication information or tokens" and "confirm that logs and screenshots do not contain tokens, cookies, authentication URLs, or account identification information" as precautions.