Raising the ceiling
on agentic coding

If it takes ‘x’ effort to get to 80%, it takes 10x the effort to get to a 100.

In HackerRank’s decade-long journey, we have worked with over 2500 companies across a variety of sectors to help hire the best developers for a variety of specialised engineering roles.

With HackerRank Bench, we point all that accumulated expertise towards creating a one-stop shop for precision software engineering data. Singular objective - solve for the last mile of agentic coding capabilities.

We handle private benchmarking, long-horizon task creation tuned to specific pre decided objectives, setting up reproducible environments with traces, and we leverage our 30mn dev community to create precision datasets for frontier labs at scale.

The reality today is that the exponential growth of agentic coding has given engineering teams a false feeling of productivity.

In terms of the overall volume of generated code, the proportion that is AI generated has rapidly climbed to over 50%. Google in fact recently reported that a staggering 75% of their code today is now AI generated.

Superficially, it looks like this is exactly what product managers and senior engineers would be excited about given the faster speed to deploy. But look a little closer and the picture begins to unravel.

• 51%

  That’s the highest mean verified accuracy on HackerRank Bench, tested September 2026. This is across complex long-horizon SWE tasks.

  HackerRank Bench, September 2026
• 22%

  That’s the recovery gap between passed (95%) and failed tasks according to Snorkel.AI’s analysis of 8 models in 2026.

  Snorkel.AI, 2026
• 70%

  That’s the incremental number of major issues that CodeRabbit reports when it comes to comparing AI generated code with pure human written code in 2026.

  CodeRabbit, 2026

The reasons for the above are broadly as follows

1. AI has become exceptional at producing code that looks passable

   When a real person submits poor code, the code is easy to reject. But what coding agents have mastered is the illusion of perfect code - reasonable naming conventions, superficial error handling and testing, and familiar patterns.

2. Long horizon thinking is still a gap

   A database query could work perfectly for a small DB when it was generated by AI and subsequently tested, but quickly changes when the DB size grows to a few million rows. Testing is now limited to how the code behaves for that specific set of assumptions, but whether the assumptions were really relevant becomes much more overlooked than earlier.

3. Accelerated implementation without accelerating verification

   What has compounded the problem is the speed of proliferation of AI code rollout. Before AI coding was deeply integrated in our workflows, engineers used to spend a serious amount of time understanding the problem from all angles. AI eliminated that process. We move faster. We break more things.

Last mile polish is the need of the hour when it comes to long horizon agentic SWE. This is time-critical as we are getting more reliant on AI generated code at a rapid pace. This AI generated code also becomes the next layer of training data for frontier models which could make this a difficult plateau to push through if done gradually, rather than if it were meticulously solved for today.

HackerRank’s unique vantage point evaluating developers on long horizon real world tasks in a variety of verticals, coupled with our massive developer pool, gives us a very specialised approach to solving this problem.

This is why HackerRank Bench was created.

The hard part isn’t complexity. It’s identifying and grading the steps in between.

Specifically, strategic decomposition. The agent needs to think through the next useful action, use feedback to recover from mistakes, and carry the work through to a high quality result. That is the signal we care about.

In a crowded space of benchmarks and data providers, we differentiate ourselves with a narrow focus on:

• Real-world, long-horizon, complex software engineering tasks and evaluation
• Grounded task design courtesy of in-depth experience in a variety of verticals
• Multi-step problems to check for resilience and recoverability
• Reproducible / resettable custom environments with detailed traces
• Evaluation frameworks calibrated for partial credit
• Assessing whether the assumptions hold up in different conditions

Private, long horizon, SWE tasks that public coding benchmarks often miss.

In our results, the strongest models delivered only half the targeted behavior, and most completed less than 40%. The summary is as follows:

Original tasks

All written from scratch. No solutions in any pre-training data.

Complete systems

Including greenfield builds, brownfield changes, migrations, and privacy sensitive work.

Same environment

Same environment for every model to ensure accuracy and reproducibility.

Scored on behaviour

Every verifier written by hand - designed specifically to check how the software behaves, and not how the code is structured.

To elaborate on the kind of demanding SWE tasks we ran:

Over the last decade, HackerRank has built an extensive library of 2000+ questions with over 470M graded submissions.

• 2000+questions
• 470Mgraded submissions
• 5Mnew submissions every month

To be clear, this is specific to our community data and does NOT include any of our customer data which is and always will be confidential.

Our library spans all the major programming languages.

Rough breakup for the major languages

• 22%Python3
• 21%pypy3
• 12%Java
• 11%mysql
• 9%C
• 8%C++
• 7%Java8
• 6%Db2
• 5%Java15
• ~25more

We also have a long tail of approximately another 25 languages.

Each submission has:

• The problem statement, constraints, and a reference solution
• Hidden test cases with expected outputs and a checker/judge
• Code submitted, Verdict, Runtime, Memory and Test-by-Test results
• Edit history between user’s attempts
• Difficulty ratings along with topic tags and the skill level

The pool of submissions grows by 5mn each month, so we have the capability to get trajectories on custom problems for any specific use-case as well.

Executable environments for repository-scale, long-horizon agent training and evaluation. Built on HackerRank’s experience creating practical, verifiable engineering assessments.

1. 01Task specRealistic, long-horizon objective
2. 02Seeded workspaceDeterministic reset
3. 03Agent toolingRead, write and submit tools
4. 04Hidden verifierNot visible to the agent
5. 05Partial creditWeighted checks, reward 0 to 1
6. 06Eval evidencePer-step logs, per-check results

GDPR Right-to-Erasure Gym

A reinforcement-learning environment for training a coding agent to erase one customer on request. The application is a multi-tenant payments platform. Merchants keep customer profiles, payments, invoices, support records, and notifications, and those records are copied across the platform’s operational stores.

The agent edits the implementation in a fresh workspace and submits it. A deterministic verifier returns a reward from 0 to 1. In benchmark runs, the highest score was 39.37%. The task requires coordinated changes across privacy, financial integrity, tenant isolation, asynchronous processing, and event replay.

Example agent run

An unparalleled expert network built over the last decade

• 31M+developers
• 600K+unique monthly visitors
• 600+developers submitting tasks monthly

With over 31 million developers, and over 600 thousand unique visitors on the platform every single month, we are very well positioned to find the best expert tailor matched to each specific requirement.

In terms of payouts, we typically have over 600 developers submitting curated tasks every month - a figure growing by approximately 10-20% every month.

In terms of expertise, our expert network comprises of approximately:

Earn with HackerRank

Real evaluation tasks from frontier labs, scoped and paid per task.