---
title: Your coding agent passes the tests. It still did not finish the job
description: A new benchmark built from real user sessions finds the best coding agent fully finishes barely a quarter of tasks, and research this week shows more tests and more debugging context are not reliable fixes.
canonical: https://hangingcontext.com/stories/2026-11-11/coding-agents-pass-but-dont-finish/
last-updated: 2026-11-11
url: https://hangingcontext.com/stories/2026-11-11/coding-agents-pass-but-dont-finish/
published: 2026-11-11T00:56:00Z
edition: 2026-11-11T00
newsroom: artificial-intelligence
beat: developers
trust: confirmed
source_count: 5
cited_manifest_ids: ['1794342415652927246', '1794304109607666712', '1794304109607666717', '1794304109607666710', '1794331194496831889']
---

# Your coding agent passes the tests. It still did not finish the job

*A new benchmark built from real user sessions finds the best coding agent fully finishes barely a quarter of tasks, and research this week shows more tests and more debugging context are not reliable fixes.*

Confirmed · 5 Manifests · Artificial Intelligence · Published 2026-11-11 00:56 UTC

## Key facts

- GPT-6 Astra passed 69.9% of criteria per task on average but fully finished only 26.4% of tasks on Co-Design Bench, leaving at least one criterion unmet in 73.6% of tasks. [cites: 1794342415652927246]
- Co-Design Bench consists of 273 tasks and 2,331 hidden acceptance criteria, curated from 37,463 deduplicated queries across 26,314 real user coding sessions. [cites: 1794342415652927246]
- In a preprint on agentic debugging, bounded debugging context lifted the aggregate pass rate by 19.0 percentage points, yet even faithful context can anchor a strong model on the wrong layer of the system. (self-reported) [cites: 1794304109607666712]
- In a preprint on agent-generated tests, test-writing frequency does not correlate with task resolution success, and forcing test generation increases API and token usage without improving resolution outcomes. (self-reported) [cites: 1794304109607666717]

## Mostly right is not done

Co-Design Bench, a benchmark published on 26 Oct 2026 and created by researchers at Alibaba and five universities, measures something most coding benchmarks skip: whether the agent actually finished the task, not just whether it passed some checks. It consists of 273 tasks and 2,331 hidden acceptance criteria, curated from 37,463 deduplicated queries across 26,314 real user coding sessions. The best-performing model, GPT-6 Astra, passed 69.9% of criteria per task on average, but fully finished only 26.4% of tasks. In 73.6% of tasks, at least one criterion stayed unmet. Opus 5 fully finished 18.3% of tasks, and even the lowest-scoring model passed 51.1% of criteria. [cites: 1794342415652927246]

The gap matters because of how code review works. Diff-based reviews show what changed, so a requirement the agent quietly skipped is easy to miss, and incomplete code gets merged looking complete. Astra finished 41.0% of 100 multimodal editing tasks, which is better than its overall rate but still leaves most tasks partly undone. The benchmark also exposes how fragile rankings are: swapping harnesses moved Qwen3.8-27B between 56.0% and 74.9% on repository edits, so a leaderboard number says as much about the harness as about the model. [cites: 1794342415652927246]

## Debugging help helps, until it misleads

A preprint on agentic debugging of distributed systems, DDBench, tests a different part of the same problem: finding the bug in the first place. Distributed debugging exercises a reasoning dimension that single-process benchmarks do not surface, and bounded debugging context lifts the aggregate pass rate by 19.0 percentage points. That is a real gain, and it comes with a warning attached: even faithful debugging context can anchor a strong model on the wrong layer of the system when its observation sits far from the fault. More context is not the same as the right context, and context that looks helpful can lock the agent into a wrong theory. [cites: 1794304109607666712]

> **From Synorb** — Ask what Synorb covers. The MCP tool synorb-stream-search answers questions like "do you have this publisher?" without using any Manifest allowance. [Connect and ask](https://synorb.com/connect/start)


The practical read is that debugging agents need curation, not volume. A team that dumps logs and traces into the prompt and hopes the model sorts it out is as likely to get a confident wrong answer as a correct one. The benchmark's point is that distributed bugs are a distinct skill, and a model that is strong on single-file fixes can still be weak when the fault lives across services. [cites: 1794304109607666712]

## Writing more tests does not fix it

Another preprint this week asks whether agent-generated tests help at all, and the answer is uncomfortable for teams that treat test-writing as a safety net. The authors found that test-writing frequency is model-dependent and does not correlate with task resolution success. Agent-generated tests prioritize printing runtime values over logical assertions, which means they often confirm the code ran rather than confirming it is right. Forcing test generation significantly increases API and token usage without improving resolution outcomes, so the habit costs money and buys little. [cites: 1794304109607666717]

A related preprint on patch correctness makes the same point from the evaluation side. Reported detection rates in automated program repair and coding agents are significantly altered by changes in candidate populations, oracle rules, or execution budgets, and repository-level coding agents require distinct evaluation protocols that separate issue reproduction from candidate-patch discrimination. In plain terms, a passing test suite is a weak claim about a patch, and the number moves when the rules of the test move. [cites: 1794304109607666710]

## The coverage gap underneath

Longtail-SWE, a preprint benchmark of 202 issue-resolution tasks, shows where the measurement itself is thinnest. It covers 13 programming languages with limited coverage in existing repository-level benchmarks: Clojure, Dart, Elixir, Erlang, F#, Haskell, Julia, Kotlin, Lua, OCaml, R, Scala, and Swift. The authors built it with a solver-judge-repairer pipeline, evaluated nine model configurations including frontier models, and released executable environments, construction and evaluation tooling, and agent trajectories, plus an easier set of tasks for smaller models. If your stack lives outside the mainstream languages, the headline benchmark numbers were never really about you. [cites: 1794331194496831889]

Taken together, the week points to one decision for teams shipping with coding agents. Completion, not pass rate, is the number to track, and it should be measured against hidden acceptance criteria the agent never saw, on your own repositories, in your own languages. Tests the agent wrote itself, and reviews that only show the diff, will both flatter a result that is not finished. [cites: 1794342415652927246]

## What is still unresolved

- Whether completion rates improve when agents are given explicit acceptance criteria up front.
- Whether curated debugging context can be generated reliably, or only hand-picked.
- How completion rates look in the 13 Longtail-SWE languages compared with mainstream ones.

## Decision card

- **Useful for:** Developers shipping code written by agents; Engineering managers setting review policy for agent output; Teams choosing a coding agent on evidence, not leaderboards
- **Not for:** Teams that only need short, single-file completions; Anyone treating a passing test suite as proof of completion
- **Compare against:** Measuring full-task completion on your own repositories; Reviewing the full requirement list, not just the diff

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794342415652927246` | The Best Coding Agent Passed 70% of Acceptance Criteria but Fully Finished Only 26.4% of Real Tasks. Who Checks the Rest? | [qodo-publishing](https://qodo.ai/blog/alibaba-co-design-bench-coding-agents) | 1794342720876930663, 1794342720878930664, 1794342720885930669, 1794342720886930670, 1794342720888930671, 1794342720889930672, 1794342720879930665, 1794342720881930666, 1794342720882930667, 1794342720883930668, 1794342720891930673, 1794342720897930678 | 11-10 00:00 |
| `1794304109607666712` | DDBench: A Benchmark for Agentic Debugging on Distributed Systems | [arxiv-code-devtools-ai](https://arxiv.org/pdf/2608.14863v2) | 1794304358342670472, 1794304358344670473, 1794304358340670471 | 11-10 00:00 |
| `1794304109607666717` | Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents | [arxiv-code-devtools-ai](https://arxiv.org/pdf/2602.07900v3) | 1794304354671669988, 1794304354691669990, 1794304354683669989 | 11-10 00:00 |
| `1794304109607666710` | Patch correctness in automated program repair: A five-dimensional evidence framework and cross-benchmark audit from classical repair to coding agents | [arxiv-code-devtools-ai](https://arxiv.org/pdf/2611.10837v1) | 1794304358503670529, 1794304358513670531 | 11-10 00:00 |
| `1794331194496831889` | Longtail-SWE: High-Quality SWE Tasks for Underrepresented Languages | [huggingface-daily-papers](https://huggingface.co/papers/2611.10304) | 1794331429989833425, 1794331429991833426, 1794331429993833427, 1794331430011833432, 1794331430019833433, 1794331430023833434 | 11-10 00:00 |

## Run these Manifests yourself

Every Manifest cited here can be read in full with your own Synorb Key. Check the claims before you rely on them.

1. **Get a Synorb Key.** Click Generate Free Agent Key at https://synorb.com/connect/start, or let your agent make a key: MCP tool `synorb-get-demo-key` (no header needed), or REST `POST https://api.synorb.com/api/connect/demo-key` with a JSON body such as `{"client":"your-agent-name"}`. The key is the `api_key` field of the answer. Save the key. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons.
2. **Read each cited Manifest by id.** REST: `GET https://api.synorb.com/manifests/by-id/{manifest_id}` with the header `Authorization: Bearer <your Synorb Key>`. Ids are strings; do not parse them as numbers. The reply holds the source, the Signal (claims) and the Brief.
3. **Check the claim.** Match each sentence in the story to the claims in the Manifest it cites. The claim ids in the ledger point to them.

Cited Manifest ids:

- `1794342415652927246`
- `1794304109607666712`
- `1794304109607666717`
- `1794304109607666710`
- `1794331194496831889`

```sh
for id in 1794342415652927246 1794304109607666712 1794304109607666717 1794304109607666710 1794331194496831889; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

> **From Synorb** — Run the cited Manifests yourself. Every claim in this paper points to a Manifest id. Get a Synorb Key and read the Manifests behind it. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons. [Get a key](https://synorb.com/connect/start)

## Related stories

- [Coding agents meet the distributed system, and the context turns against them](https://hangingcontext.com/stories/2026-11-10/coding-agents-distributed-debugging/)
- [Better reasoning is getting cheaper to train](https://hangingcontext.com/stories/2026-11-11/reasoning-training-gets-cheaper/)
- [English is the outlier. The models are finally catching up](https://hangingcontext.com/stories/2026-11-11/multilingual-ai-beyond-english/)
- [Gemini 4 Carbon sells itself on security and stamina, while a rival proves narrow beats broad](https://hangingcontext.com/stories/2026-11-11/gemini-4-carbon-agent-benchmarks/)

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