ClickUp Certified Agents Score 96, With Competitors Far Behind at 44-61

Most buy vs. build conversations for today's agentic platforms are driven by demos, anecdotes, and screenshots rather than data-driven evidence.

Leaders are being asked to fund aggressive AI roadmaps without hard data on how different agent platforms perform on real work. The result is predictable: impressive pilots, underwhelming execution, and a slow erosion of trust in the C suite.

To move that conversation out of the hype cycle, we ran a focused benchmark around a single, high impact workflow:

Can an agent turn a real project brief into an execution ready project plan inside a work execution tool, with minimal human glue?

We evaluated three categories of approaches:

• A ClickUp Certified Project Plan Builder Agent
• ClickUp Super Agents
• Microsoft Copilot, ChatGPT with connectors, Notion agents, and Monday agents.

Every agent was provided the same underlying project briefs.

Every agent was scored against the same zero to one hundred framework across six criteria that real teams feel when they try to run work:

• Project source and detection – can the agent find and use the right brief without copy paste.
• Structured project plan creation inside a work tool – does it create a real plan inside the system of record, not just text someone has to retype.
• Task dependencies and sequence integrity – can it build a plan that respects reality instead of a flat list.
• Baseline metrics documentation – does it give leaders something they can actually manage against.
• Communication and visual output – can both PMs and executives understand what is going on.
• Professional tone and clarity – would you put this in front of a leadership team.

Here's what we found.

Our Benchmark Reveals 4 Key Findings

When we stepped back and asked, “If you were a CIO betting your reputation on one agent platform to run project portfolios, who would you trust?”, the pattern was clear:

ClickUp Certified Agents were the only agents that consistently hit “plan ready to run” across all six criteria.

• They read the brief from the source, created rich project structures in ClickUp, wired dependencies, and documented baselines in ways leaders can use.

ClickUp Super Agents performed well as a strong baseline build.

• They detected context automatically and created good plans inside ClickUp, with solid baselines and clear communication. The benchmark gap between Super and Certified Agents was about default instrumentation and repeatability, not about basic capability.

Copilot and ChatGPT could get into the game, but only after meaningful integration work.

• Without careful wiring, they produced good narratives but thin plans inside the work tools.

Notion and Monday agents struggled on the core objective.

• They could draft tasks and lists, but left a lot of stitching and cleanup work on human teams.

The takeaway is not that competitors cannot be made to work. It is that you pay for that performance with extra configuration, custom integration, and ongoing maintenance. Even then, teams still end up filling key gaps by hand.

Taken together, these results highlight three practical questions every leader should ask about agent platforms:

1. How much human glue is required?
If your teams must copy-paste briefs, manually wire integrations, and rebuild plans in your work tool, you’re not getting the full value of AI.

2. How close does the agent get you to execution? An impressive narrative is useful; an execution-ready plan inside your system of record is transformative.

3. What is the real cost of ownership? Platforms that require heavy integration work or constant human cleanup increase operational risk and reduce the net ROI of AI initiatives.

In this benchmark, ClickUp Certified Agents and the Super Agent platform they run on consistently reduced the distance between idea and execution. Competitors could achieve similar outcomes with enough effort, but often at the expense of complexity, fragility, and human time.

For mid-market and enterprise organizations, where portfolios are large and teams are cross-functional, that difference compounds quickly.

This report is meant to give CIOs, business systems leaders, and PMO or operations teams a practical lens. Not “Which AI is smartest,” but “Which agent is ready to run our work?”

Each agent was provided the same initial prompt to build the agent.

Each agent was asked to generate a project plan from the same underlying project briefs, covering realistic, multi phase initiatives.

The goal was simple:

• Ingest the brief from the primary work execution tool.
• Create a structured plan in that tool that teams can actually act on.
• Capture context, dependencies, and baselines for leaders to manage risk and performance.

Agents Evaluated

ClickUp Certified Project Plan Builder Agent
A refined, evaluated agent built specifically to turn project briefs into execution ready plans inside ClickUp. It went through multiple rounds of prompt design, testing, and scoring.

ClickUp Super Agents (baseline builds)
Super Agents created from a single shared starting prompt. They represent what a typical team might build on a first pass using ClickUp’s Super Agent platform.

Microsoft Copilot connected to work tools through custom flows and integrations.

ChatGPT with connectors pushing plans into external tools.

Notion agents operating inside Notion’s workspace.

Monday agents built with Monday’s Agent Factory.

Every agent was asked to solve the same underlying problem. Where platforms needed more configuration, we counted that effort as part of the qualitative analysis.

How We Ran the Benchmark

Yes, we benchmarked ourselves. That deserves a clear explanation.

We believe the only honest way to do this is to be very specific about how the test worked, what was scored, and where you should scrutinize the numbers for your own environment.

Key principles:

• Same scenario, same briefs. Every agent faced the same real world project briefs.
• Six concrete criteria. We used a zero to one hundred rubric covering project source detection, plan creation, dependencies, baseline documentation, communication, and professional clarity.
• LLM and human review. We combined model based scoring with human checks for repeatability and fairness.
• Integration and setup count as a cost. If a platform needed heavy manual setup or integration to work, that counted against it.

This is a transparent, repeatable process. If you want to challenge or replicate the results, we invite you to reuse the scoring criteria and run the same workflow inside your own tools.

Scoring Criteria

Once each agent was built, they were asked to create a project plan from the same underlying briefs. We scored performance using a consistent 0–100 framework across six core criteria that matter to real-world execution:

Project Source & Detection

• Can the agent automatically find and use the right brief from the primary work tool without manual copy-paste?

Structured Project Plan Creation in a Work Execution Tool

• Does it create a real plan inside the work system, not just narrative text?

Task Dependencies & Sequence Integrity

• Can it build a logically sequenced plan with dependencies where needed?

Baseline Metrics Documentation

• Does it capture scope, schedule, and effort baselines in a way leaders can track?

Communication & Visual Output Quality

• Are results presented clearly enough for both project managers and executives?

Professional Tone & Clarity

• Does the output read like something you would put in front of a leadership team?

Certified Agent benchmark score: 96 out of 100.

Across every evaluation area, the ClickUp Certified Project Plan Builder Agent consistently produced plans that were closest to truly execution-ready:

• It could be triggered directly from within ClickUp, automatically pulling the full project brief and relevant context from the same task, list, or doc.
• It created rich, structured project plans inside ClickUp—not just a narrative in a chat window—with meaningful task descriptions, phases, and milestones.
• It was the only agent in the benchmark to reliably create task dependencies in the work tool, giving teams true sequence integrity.
• It documented baseline metrics for scope, schedule, and effort in a way that leaders could reference later.

In practical terms, teams could move from idea → plan → execution in a single pass, with minimal human cleanup. That’s what makes the Certified Agent stand out in this benchmark.

Super Agent benchmark range: 78 to 86 out of 100.

ClickUp Super Agents (baseline builds) constructed from a single shared starting prompt performed well on several dimensions:

• Like Certified Agents, they were able to detect and ingest project context directly from ClickUp, avoiding manual copy-paste.
• They created project plans inside ClickUp rather than leaving work stranded in a chat.
• They produced good baseline documentation, summarizing scope, schedule, and effort in ways that could be reused.

Where they fell short compared to Certified Agents was in default instrumentation and sequence integrity:

• Dependencies were technically within reach, but the baseline configuration used in this benchmark did not explicitly prompt for them—so these runs were not counted as dependency-successful.
• Some plans were lighter on task-level detail, requiring more human follow-up to be truly “board-ready” for complex initiatives.

This is exactly what you would expect from a first-pass, do-it-yourself build: powerful, but not yet tuned to enterprise-grade consistency.

Copilot and ChatGPT benchmark range: 55 to 61 out of 100.

Microsoft Copilot and ChatGPT with MCP/connected flows demonstrated strong underlying language capabilities and could participate in the project-planning workflow, but with important caveats:

• They could pull project context—but only with significant custom integration work and carefully defined flows.
• They were able to push project plans into connected tools, but much of the richness of those plans stayed outside the work execution environment.
• In baseline scenarios, only basic task structures and minimal metadata made it into the destination tool.
• Significant custom integration work required for automating higher quality project plan creation in destination tool.
• Their performance on structured plan creation and baselines was therefore highly dependent on the depth and quality of the integration, not just the model’s reasoning.

For leaders, this lands them in a ⚠/caution band: promising platforms that can be made to work well, but only with substantial effort, configuration, and ongoing maintenance.

Notion agents benchmark range: 44 to 49 out of 100. Monday agents benchmark score: 44 out of 100.

In this benchmark scenario, Notion agents and Monday agents showed clear limitations when judged against end-to-end execution readiness:

Monday agents had no native way to connect with the Monday platform.

• Could not automatically detect and pull project context from existing Monday work items. Plans depended on manually supplied information.
• Required manual project plan export to CSV file format in order to import into Monday platform.
• For these reasons Monday failed in automating both new project detection and project plan creation.

Notion agents could access project data stored in Notion, but:

• Could not be triggered in an equally seamless, in-context way.
• Created plans that were often sparse in detail and always defaulted to private, requiring additional manual work to make them visible to teams.

Both platforms scored lower on baseline documentation and communication richness, forcing PMs and leaders to spend more time reconstructing the “story” of the project from scattered details.

The net result: these agents introduced more friction between brief and execution, even when they technically “completed the task.”

Two criteria emerged as especially important for leaders who care about governance and risk:

1.Task Dependencies & Sequence Integrity
Only ClickUp Certified Agents consistently created dependencies in the work tool. Super Agents can do this as well, but weren’t explicitly configured for it in this baseline run. Other platforms recommended little or no sequence information.

2. Baseline Metrics Documentation
While most agents reached at least an average performance, ClickUp Certified Agents and Super Agents were the only ones to reliably encode baselines in structured, reusable ways. Notion, in particular, fell behind with minimal baseline detail.

Without these two capabilities, you end up with lists of tasks rather than true projects—and leaders lose the ability to manage variance, trade-offs, and risk at scale.

On Communication & Visual Output Quality and Professional Tone & Clarity, most agents did reasonably well:

• Copilot, ChatGPT, and both ClickUp agent types all produced outputs that would be acceptable in most executive contexts.
• ClickUp Certified Agents went a step further, structuring outputs with stakeholder-friendly summaries and clear domain groupings.
• Notion lagged here, with sparse communication that created more follow-up questions than answers.

This reinforces a key point: language fluency is now table stakes. The differentiator is whether the agent can structure and instrument work so that humans can immediately move to execution.

Finally, when we look across criteria, a pattern emerges:

• ClickUp Certified Agents minimize human glue, reduce integration burden, and move you closest to an execution-ready plan with one run.
• Super Agents get you close, especially in ClickUp-native environments, and are powerful starting points for teams who want to tune their own agents.
• Copilot and ChatGPT with integrations can approximate this, but the path involves more custom wiring, testing, and ongoing monitoring.
• Notion and Monday currently leave the most work on the table for humans to stitch plans together.

For CIOs and business leaders, this is where the benchmark becomes strategic: the closer an agent gets you to reliable, instrumented plans with minimal effort, the more it compounds across your entire project portfolio.

The benchmark results are one signal. Underneath them is a platform approach designed for enterprise-grade AI adoption.

Built for Work Execution, Not Just Conversation

ClickUp’s Super Agents are embedded directly into the work execution layer:

• They run where your tasks, docs, and workflows already live.
• They can read and write structured data (lists, tasks, fields, dependencies) instead of treating everything as text.
• They are designed to support both individual contributors and portfolio leaders with the same underlying model of work.

Certified Agents as a Reliability Layer

Certified Agents, like the Project Plan Builder evaluated here, go through:

• Rigorous prompt design and refinement
• Structured evaluations across multiple scenarios
• Iterative tuning based on benchmark results

The result is not just “a better prompt,” but a higher-confidence agent with:

• Predictable behavior across common project types
• Stronger defaults for baselines, dependencies, and communication
• A tighter feedback loop between product, solutions, and customer outcomes

Designed to Support Your AI Strategy

For CIOs and business leaders, the key is alignment with an overall AI strategy—not one-off wins.

ClickUp’s Super Agent platform is built to:

• Support centralized governance over how agents access data and act in your workspace
• Enable specialized agents (like Certified Project Plan Builders) that can be rolled out safely at scale
• Provide a foundation for ongoing benchmarking and improvement, so your agents get better over time instead of drifting

In other words: the same capabilities that led ClickUp Certified Agents to perform strongly in this benchmark are the ones that help organizations bring their AI strategy to life in production.

If you are evaluating AI agents for project planning and execution, consider these next steps:

1. Review the benchmark with your core stakeholders. Map the criteria in this report to your own portfolio, governance model, and risk profile.

2. Identify where you rely on human glue today. Look for copy paste workflows, manual integrations, and unmanaged spreadsheets that slow down execution.

3. Run your own benchmark. Use the criteria and patterns in this report as a template to test agents against your own briefs and tools.

4. Explore Certified Agents and Super Agents in a guided session. See how the Project Plan Builder and related agents behave against one of your real project briefs.

5. Use benchmarks as a living measure of progress. As you roll out agents, keep testing. Track how close each agent brings you to execution ready work over time.

AI agents will increasingly shape how your organization plans, executes, and learns.

This benchmark is meant to help you avoid paying for demo ware and integration tax, and instead invest in agents that reliably run the work.