{"slug": "compression-should-be-part-of-your-agent-harness", "title": "Compression should be part of your agent harness", "summary": "Compression should be built into the agent harness rather than triggered only when the context window fills, according to an analysis of three open-source projects: RTK, Caveman and Ponytail. RTK reports up to 90% less command output through deterministic techniques such as filtering, grouping, deduplication and JSON minification, while Caveman shrinks tool descriptions and generated explanations and Ponytail compresses generated code. The tools target different points in the agent loop, and the analysis argues developers should evaluate each component separately for input size, preserved error details and answer quality.", "body_md": "# Why compression should be part of your agent harness\n\nEvery developer I know is complaining about hitting their AI coding quotas in Claude, Cursor or Codex. Meanwhile, compression tools and skills promise to reduce how much your agent reads and writes, with [RTK reporting up to 90% less command output](https://github.com/rtk-ai/rtk/blob/5538d72b1d0b1051751734777a61abda2cf88c4a/docs/guide/resources/savings-explained.md). That sounds promising, but how does it work and where does it fit in your agent loop? Let's look at what RTK, Caveman, and Ponytail teach us about reducing token usage without losing important context.\n\n## Why compression?\n\nDevelopers like you and me are using AI to build all sorts of applications, but we're spending less time looking at how much content our agents produce and consume. A single test run can return hundreds of lines of progress indicators, dependency downloads, successful tests, and repeated warnings, with the actual failure buried near the bottom. The same happens with large JSON responses, attached documents, retrieved pages, and handoffs from other agents, which your harness assembles into the next model request even when only a fraction is useful. If it only compresses the conversation when the context window fills up, the agent has already spent tokens reading everything.\n\nWe spend a lot of time on context engineering around the model, but pay very little attention to how we prepare the data going into those models. As a result, we end up feeding terminal output, entire documents, and verbose API responses into an agent's working context, even when only a small part of that information is needed. This doesn't become much of a problem if you have an unlimited token quota or use very little AI, but most of us end up running out of our quotas regularly.\n\nI believe **compression should be a standard part of your agent harness**, not something that only kicks in when you're running out of context. By harness, I mean the code that calls the model, executes tools, and assembles the next request. That code already decides what the model sees, so why shouldn't it also decide how to represent that information? When talking about context compression, you should distinguish between deterministic and semantic compression. Deterministic compression doesn't require an LLM and can reduce the context by applying filtering, grouping, deduplication, and careful formatting. These techniques can reduce the amount of context while keeping the information the agent needs, while semantic compression uses a model, which could be a small local model, to rewrite the text and keep the details needed for the task.\n\nDifferent tools apply compression differently, so let's look at how these three projects approach it.\n\n### What each tool changes\n\n[RTK](https://github.com/rtk-ai/rtk), [Caveman](https://github.com/JuliusBrussee/caveman), and [Ponytail](https://github.com/DietrichGebert/ponytail) apply compression at different points in an agent's loop, with integrations for popular coding agents. Each project includes different components, so the component matters as much as the project name.\n\n| Project or component | What it changes | What to evaluate | \n|---|---|---|\n| RTK command filters | Tool output before the model reads it | Input size and required error details | \n| RTK minify-json method | JSON content before the model reads it | Input size and preserved values | \n| Caveman description shrinker | Tool descriptions | Input size and preserved instructions | \n| Caveman response skill | Generated explanations | Input and output usage and answer quality | \n| Ponytail coding skill | Generated code | Correct behavior, readability, full usage, and cost | \n\nBefore jumping into the examples, we need to separate compressing existing input from asking an agent to write less, because those approaches change different parts of the model request.\n\n## Deterministic compression\n\nFirst, start with context that does not need a model to compress it. For example, a build log can contain several kinds of information but most of it isn't relevant to your coding agent. A failing test name matters as it can point the agent to the code it needs to fix, whereas download progress usually matters less once the download has finished. So why would you pass all the download information to the LLM? Instead, you can use a deterministic compressor that applies explicit rules to every piece of context. These rules are defined in whatever programming language you use for the harness. The input can come from a tool, a user-supplied file, or a retrieved resource. The rules decide what to keep without asking a model which lines look useful, and can do things like:\n\n- Filter recognized download progress while preserving warnings and diagnostics.\n- Group identical successful events and include their count.\n- Select fields from a known response schema for a specific task.\n- Parse JSON and serialize it without indentation.\n\nFor reference, [RTK's command filters](https://github.com/rtk-ai/rtk/blob/5538d72b1d0b1051751734777a61abda2cf88c4a/docs/guide/resources/what-rtk-covers.md) give a good impression of deterministic compression techniques.\n\nThese operations keep different amounts of information, so you should always preserve the original files in case the coding agent needs more details. Dropping fields can remove something it needs, while grouping events into a count loses their order and may leave out details needed to investigate what happened. Removing insignificant whitespace from JSON is different from stripping spaces out of Python or changing whitespace inside a string. But on the other hand, if your agent just needs to know if the build completed and how long it took, you can seriously decrease the amount of context it sees.\n\nLet's look at how **RTK 0.49.0** handles these inputs, using [this version of the code](https://github.com/rtk-ai/rtk/tree/5538d72b1d0b1051751734777a61abda2cf88c4a) for the examples below.\n\n**How I counted tokens:** I used `tiktoken 0.12.0` with `cl100k_base` throughout. The RTK examples count the complete returned text, with JSON content and hook notes shown separately. The skill comparison counts the task, full skill instructions, and response from one run per skill. These are text comparisons rather than full agent usage or billing, which also depend on the rest of the request, hidden reasoning, and caching. The skill runs were configured with the Codex runner's `gpt-6.1-sol` label and low reasoning effort, but no separate provider model ID or usage was recorded.\n\n### Remove JSON formatting with minify-json\n\nSay a user supplies this build summary as a JSON file, or a tool returns it:\n\n```\n{\n  \"service\": \"checkout-api\",\n  \"status\": \"failed\",\n  \"tests\": {\n    \"passed\": 4,\n    \"failed\": 1\n  },\n  \"failure\": {\n    \"test\": \"test_retry_budget\",\n    \"expected\": 3,\n    \"actual\": 4\n  },\n  \"message\": \"retry budget exceeded\"\n}\n```\n\nRTK offers two different ways to reduce this input, starting with its [`minify-json` method](https://github.com/rtk-ai/rtk/blob/5538d72b1d0b1051751734777a61abda2cf88c4a/engine/src/methods/minify_json.rs), which reads JSON and writes it back without indentation. You can use the smaller result when preparing context for a coding agent, with the following RTK pipeline showing the Claude Code Read hook used for this example:\n\n```\nschema_version = 1\n[pipeline]\nname = \"json-minify-demo\"\n\n[[hook.after_tool.read]]\nname = \"minify-json-read\"\nwhen = { format = \"json\" }\nuse = [\"minify-json\"]\n\n[guards]\nmin_size = 0\nnever_worse = true\n```\n\nHere, `min_size = 0` allows this small example to run even though it is below the normal 512 byte minimum, and the method produces the following JSON:\n\n```\n{\"service\":\"checkout-api\",\"status\":\"failed\",\"tests\":{\"passed\":4,\"failed\":1},\"failure\":{\"test\":\"test_retry_budget\",\"expected\":3,\"actual\":4},\"message\":\"retry budget exceeded\"}\n```\n\nThe payload goes from **77 to 42 reference tokens**, about **45.5% less**. Every parsed value in this fixture stays the same, including the spaces inside `\"retry budget exceeded\"`. On the [Microsoft Edge sample JSON](https://github.com/MicrosoftEdge/Demos/blob/e3b81daee151a225c1d8f24bf82d31c464b0f737/json-dummy-data/1MB.json) used below, a file of about 1 MB, all **3,168 records** and their parsed values also survive minification:\n\n| JSON content only | Original | RTK minify-json | Reduction | \n|---|---|---|---|\n| Small build summary, reference tokens | 77 | 42 | 45.5% | \n| Microsoft sample, reference tokens | 357,374 | 297,198 | 16.8% | \n| Microsoft sample, UTF-8 bytes | 1,026,294 | 912,253 | 11.1% | \n\nThe large file loses **60,176 reference tokens**, about **16.8%**, without removing any records.\n\nRTK labels this method lossy because duplicate keys and some large integers can change when JSON is read and written back. Both examples have unique keys and pass checks that their parsed values stay the same, so check your own data before using the same approach.\n\nBut there's a catch with the small example, because the RTK hook adds a **48-token format-conversion note**. That brings the total to **90 tokens**, which is **13 more** than the original 77, an increase of **16.9%**, even though the JSON itself is smaller. The larger example still comes out ahead at **297,246 tokens** including the note, or **60,128 fewer** than the source, a reduction of **16.8%**.\n\nThis is the kind of detail you miss when you only look at the compressed file.\n\n### Use rtk json for a readable summary\n\nNow run the other RTK path over the same build summary:\n\n```\nrtk json build-summary.json\n```\n\nRTK produces:\n\n```\n{\n  failure:\n  {\n    actual: 4\n    expected: 3\n    test: \"test_retry_budget\"\n  }\n  message: \"retry budget exceeded\"\n  service: \"checkout-api\"\n  status: \"failed\"\n  tests:\n  {\n    failed: 1\n    passed: 4\n  }\n}\n```\n\nOn this fixture, that is **77 to 69 reference tokens**, about **10.4% less**. The service, failed status, test counts, retry test, expected/actual values, and message remain visible. This command does more than remove whitespace because it sorts the keys and removes JSON syntax such as quoted keys and commas, so you should treat its output as a readable view rather than JSON to parse later. RTK's [JSON renderer](https://github.com/rtk-ai/rtk/blob/5538d72b1d0b1051751734777a61abda2cf88c4a/src/cmds/system/json_cmd.rs) also summarizes arrays with more than five elements, truncates long strings, and limits depth and displayed keys. The small fixture does not trigger those limits.\n\nFor a larger check, I ran the same command on [Microsoft Edge's sample JSON snapshot](https://github.com/MicrosoftEdge/Demos/blob/e3b81daee151a225c1d8f24bf82d31c464b0f737/json-dummy-data/1MB.json) mentioned before, containing **3,168 records**:\n\n```\nrtk json microsoft-1MB.json\n```\n\nThe entire output is:\n\n```\n[{\n    bio: \"Donec lobortis eleifend condimentum. Cras dictum dolor lacinia lectus vehicul...\"\n    id: \"V59OF92YF627HFY0\"\n    language: \"Sindhi\"\n    name: \"Adeel Solangi\"\n    version: 6.1\n  }, ... +3167 more]\n```\n\n| Complete source versus RTK view | Original | RTK stdout | Reduction | \n|---|---|---|---|\n| UTF-8 bytes | 1,026,294 | 209 | 99.98% | \n| Reference text tokens | 357,374 | 81 | 99.98% | \n\nThat is about **99.98% less text**, but it achieves this by displaying the first record, shortening its biography, and replacing the other 3,167 records with a count. This can help an agent inspect the shape of a dataset, but it prevents the agent from answering questions about every record, finding all matching people, or recovering the complete biography. For those tasks, the harness needs the original data or a task-specific query over it.\n\nMy advice would be to use `minify-json` when the task needs the complete values and you've checked that they survive the conversion, while using `rtk json` when a readable overview is enough. The 81-token summary cuts **99.98%** of the source tokens, while the 297,198-token minified JSON cuts **16.8%** and keeps all the records. They serve different tasks, so their reduction percentages don't tell you which is better for your agent.\n\n### Keep the failing test, remove routine progress\n\nFor a test run, the agent usually needs to know which test failed and what went wrong, while the names of the passing tests matter less when its next job is to fix that failure. To see what RTK keeps, let's use this example of a log with five tests, where four passed and one failed:\n\n```\n============================= test session starts ==============================\ncollected 5 items\ntests/api/test_checkout.py::test_read PASSED [ 20%]\ntests/api/test_checkout.py::test_create PASSED [ 40%]\ntests/storage/test_cache.py::test_hit PASSED [ 60%]\ntests/storage/test_cache.py::test_miss PASSED [ 80%]\ntests/api/test_retries.py::test_retry_budget FAILED [100%]\n=================================== FAILURES ===================================\n____________________________ test_retry_budget _____________________________\nE   AssertionError: expected 3 attempts, got 4\ntests/api/test_retries.py:12: AssertionError\n=========================== short test summary info ============================\nFAILED tests/api/test_retries.py::test_retry_budget - AssertionError: expected 3 attempts, got 4\n========================== 1 failed, 4 passed in 0.84s ==========================\n```\n\nYou can pass the log through RTK's pytest filter before adding it to the next model request:\n\n```\nrtk pipe --filter pytest < pytest.log\n```\n\nRTK returns the following summary, which keeps the failure details while removing the list of passing tests:\n\n```\nPytest: 4 passed, 1 failed in 0.84s\n\nFailures:\n1. [FAIL] test_retry_budget\n     E   AssertionError: expected 3 attempts, got 4\n     tests/api/test_retries.py:12: AssertionError\n\n2. [FAIL] tests/api/test_retries.py::test_retry_budget\n     AssertionError: expected 3 attempts, got 4\n```\n\nThis example goes from **175 to 82 reference tokens**, about **53.1% less**, while keeping the result, duration, failed test name and path, assertion, and source location. RTK includes both the failure details and the entry from pytest's final summary, which is why the same failure appears twice even though the result still says one test failed.\n\nThis gives the agent enough information to start looking at the retry failure, but it would need the original log if it wanted to know which successful test ran before it. Whether you can remove those details depends on what the agent needs to do next, which is why you should keep the original available.\n\n### Group repeated log messages\n\nAnother thing you can do without an LLM is group messages that appear repeatedly, so the agent sees how often something happened without having to read the same line over and over:\n\n```\nrtk log application.log\n```\n\nFor this example, I created a log with 51 lines, containing 40 identical `INFO worker ready` messages, eight identical `WARN retry budget nearly exhausted` messages, and three identical `ERROR checkout failed` messages. RTK turns that log into the following summary:\n\n```\nLog Summary\n   [error] 3 errors (1 unique)\n   [warn] 8 warnings (1 unique)\n   [info] 40 info messages\n\n[ERRORS]\n   [×3] 2026-10-20 09:00:02 ERROR checkout failed: expected 3 attempts, got 4\n\n[WARNINGS]\n   [×8] 2026-10-20 09:00:01 WARN retry budget nearly exhausted\n```\n\nThe complete text goes from **859 to 97 reference tokens**, about **88.7% less**, and the counts of errors, warnings, and info messages still match the original. The agent can see an example of the error and warning alongside how often each occurred, while the info messages are reduced to a count because their individual lines add little to this overview.\n\nWhat this summary no longer tells you is the order in which all the messages appeared, so an agent investigating the sequence of events would still need to read the original log.\n\n### Check the log format before accepting the result\n\nThis only works if the filter understands the format of your input, which becomes clear when you run the same command on [LogHub's Android log](https://github.com/logpai/loghub/blob/374038faa4ca122dd7ca0730372c0b78f76f8597/Android/Android_2k.log), a sample containing 2,000 lines:\n\n```\nrtk log Android_2k.log\n```\n\nThe log marks warnings with `W` and errors with `E`, and the sample contains **170 `W` records and 3 `E` records**, but RTK starts its summary with these counts:\n\n```\nLog Summary\n   [error] 6 errors (6 unique)\n   [warn] 0 warnings (0 unique)\n   [info] 34 info messages\n```\n\nThe complete output is **263 reference tokens**, down from **100,086** in the original. That's a 99.7% reduction, but the summary says there are no warnings in a log that contains 170 of them.\n\nRTK's [generic log filter](https://github.com/rtk-ai/rtk/blob/5538d72b1d0b1051751734777a61abda2cf88c4a/src/cmds/system/log_cmd.rs) looks for words such as `error`, `alert`, `warn`, and `info`, rather than reading Android's separate column for the message level. A debug message containing `alerts` gets counted as an error, and the six displayed messages are shortened as well.\n\nFor a task that needs the warning and error records, the harness rejects this summary and falls back to the original **100,086-token** log because none of those 173 records survives unchanged. I'd rather send the full log than give an agent a much smaller version that tells it there are no warnings to investigate.\n\n## Semantic compression is a different beast\n\nCaveman and Ponytail take a different approach because they give the model instructions that change what it writes, rather than applying fixed rules to something that already exists. [Caveman's response skill](https://github.com/JuliusBrussee/caveman/blob/bad48a0e1df55e05e3f52249afa35ecc1909ee76/skills/caveman/SKILL.md) asks for shorter answers, while [Ponytail](https://github.com/DietrichGebert/ponytail/blob/09c434ab73ca43761c5d8b0c5d341926c2fba710/skills/ponytail/SKILL.md) asks the model to solve the problem with less unnecessary code, so both can reduce output while adding instructions to the input.\n\nYou can see why that difference matters in Ponytail's [published benchmark report](https://github.com/DietrichGebert/ponytail/blob/16f29800fd2681bdf24f3eb4ccffe38be3baec6b/benchmarks/results/2026-06-18-agentic.md), which compared 12 feature tasks with four runs for each task and set of instructions using Haiku 4.5. Ponytail reported 54% fewer added lines of code and 22% fewer total tokens than the baseline, while the Caveman comparison produced 20% fewer lines but used 7% more tokens. It gives us a useful example of how shorter code and lower token usage can produce different results, but you would still need to check what happens on your own tasks.\n\nFor a smaller example, I gave the same task to three separate agents using the same model. One used basic instructions, one loaded the full Caveman skill in normal mode, and one loaded Ponytail in its default full mode:\n\n```\nImplement unique_ids(values) in Python. Input is a list of strings. Return a new list containing each distinct string once, preserving first-seen order. Comparison is case sensitive. Keep the input unchanged. Empty input returns an empty list. Do not add validation, a CLI, dependencies or unrelated behavior. Include a brief explanation.\n```\n\n### Compare the actual responses\n\nThe baseline used a set to keep track of strings it had already seen and added each new string to a separate list, which keeps the original order without changing the input:\n\n``` python\ndef unique_ids(values):\n    seen = set()\n    result = []\n\n    for value in values:\n        if value not in seen:\n            seen.add(value)\n            result.append(value)\n\n    return result\n```\n\nBoth Caveman and Ponytail chose the shorter version below, using Python's dictionary to remove duplicates while keeping each string in the position where it first appeared:\n\n``` python\ndef unique_ids(values):\n    return list(dict.fromkeys(values))\n```\n\nThe baseline function contains **41 reference tokens** of code, while both skill responses use the same **13-token** function, and both approaches should take roughly linear time for this task. The dictionary version works because the input contains strings that can be used as dictionary keys, so you would need to reconsider it if the function also had to accept values such as lists.\n\nThe full responses, including code blocks and explanations, came to **77 baseline tokens, 48 Caveman tokens, and 63 Ponytail tokens**. That makes the Caveman response **37.7% shorter** and the Ponytail response **18.2% shorter** than the baseline. All three implementations passed the same five behavior checks and checks that the input stayed unchanged, covering empty input, duplicates, case sensitivity, and the order of the results.\n\nBoth skills chose the same code here, which makes the cost of their instructions especially relevant.\n\n### Count the skill instructions too\n\nThe task sent to each agent contains **66 reference tokens**, and the baseline adds the instruction “Write correct, readable code and briefly explain it.” For the skill versions, I counted the full `SKILL.md` file, including its metadata, and added that to the task before comparing it with the complete response:\n\n| Example | Task + instructions | Complete response | Combined text tokens | Change vs baseline | \n|---|---|---|---|---|\n| Baseline | 76 | 77 | 153 | 0.0% | \n| Caveman | 1,232 | 48 | 1,280 | +736.6% | \n| Ponytail | 868 | 63 | 931 | +508.5% | \n\nThe percentage column compares the combined text with the baseline. Caveman's skill file contains **1,166 reference tokens**, and Ponytail's contains **802**, so both add more instruction text than they save in the reply to this small task. The shorter answer looks good on its own, but the instructions change the comparison quite a bit.\n\nThose instructions can be useful across a longer coding task, and cached input may lower their cost. You still need to **count the instructions as well as the answer** when deciding whether a skill saves tokens for your agent.\n\n## Put semantic compression beside the agent loop\n\nSome inputs are harder to reduce with fixed rules because the useful information is spread across engineering notes, explanations, or a long handoff from another agent. A semantic compressor can rewrite that text or select the parts needed for the next step, which is what Caveman's separate [Caveman Compress tool](https://github.com/JuliusBrussee/caveman/blob/bad48a0e1df55e05e3f52249afa35ecc1909ee76/skills/caveman-compress/README.md) does for text files while checking their structure and keeping a backup.\n\nThat is a different job from asking the main agent to write shorter answers, and my preference is to run it separately so the main model can focus on solving the task. You can give a local or cheaper model a specific input and a list of details it must keep, then check the result before passing it back to the agent.\n\nThe diagram below shows where this fits, with the smaller version becoming part of the context for the agent's next step:\n\nThe main model can now request another tool or resource, which sends new results through the same process, or return a final response that ends the current turn. You can skip the semantic step when the deterministic result is already useful, and fall back to that checked version if a rewrite fails.\n\nFor example, a handoff might contain a file path, a retry limit, and the instruction “do not retry authentication failures.” Dropping “not” changes what the next agent is allowed to do, so that summary should be rejected even if it cuts the text in half. Checks for required paths, numbers, and instructions help catch mistakes like this, and running the task again tells you whether the smaller context is still useful.\n\nA separate compressor also has its own input and output costs, so reducing the main model's context doesn't automatically reduce the total number of tokens used.\n\nFor example, consider this **hypothetical token calculation**, which shows the difference between using a compressed input once and using it again in later requests:\n\n```\nOriginal context:               4,000 tokens\nCompressed context:             1,000 tokens\nCompressor input + output:      5,200 tokens\n\nOne downstream use:\n  avoided main-model input:     3,000 tokens\n  total change:                 2,200 more tokens\n\nThree downstream uses:\n  avoided main-model input:     9,000 tokens\n  total change:                 3,800 fewer tokens\n```\n\nWith one use, the compression step costs more tokens than it saves, but reusing the result three times brings the total down. If the first rewrite fails your checks and you ask for another, both attempts belong in that calculation, even though the agent only receives the accepted version.\n\nKeeping this work in the harness gives you a place to check the result and count those attempts before deciding what to pass to the main model.\n\n## Why I would put this in the harness\n\nIn my post about [why an LLM doesn't need to understand MCP](https://hackteam.io/blog/your-llm-does-not-care-about-mcp), I explained that the model asks for a tool and your code handles the execution. Compression belongs in that same part of the application, where the harness already decides what information goes into the next request.\n\nA skill can tell an agent to use RTK before reading a log, and skills like Caveman and Ponytail can help it produce better answers with less unnecessary text or code. But I wouldn't make preparing every incoming result another task for the main model, especially when it is supposed to be fixing a bug. If the model has already read the full log, asking it to shorten that log won't undo the input tokens you just paid for.\n\nCoding agents make this particularly important because they read files, change code, run tests, and inspect the results repeatedly. A long log left in the conversation may appear in several later requests. The harness can reuse a checked summary until the source changes, then build a new one from the latest results, keeping the original available for a later turn or another agent. You also avoid shortening the same summary again and again until useful details disappear.\n\nA plugin is a perfectly good way to provide this behavior if it filters results before the model reads them and handles the originals and checks. What matters is where compression runs, and I'd make it part of preparing each request rather than something the agent has to remember while working on the task.\n\n## How to add compression to your harness\n\nIn these tests, I reduced the large JSON by **16.8% including the hook note**, pytest output by **53.1%**, and repeated log output by **88.7%**. Showing that applying compression can have real impact on the token consumption of your agent, but only when applied at the right time and the right type of context. When adding compression to your own harness start with one noisy input your agent sees regularly, such as a test log or a large JSON response, and work through these steps before applying the same rules more widely:\n\n1. \n**Keep the original.** Store the full input, where it came from, and the exit status for tool calls, so the agent can ask for details that the smaller version leaves out.\n2. \n**Separate instructions from data.** Keep the user's request and system rules intact, and target supporting data such as logs, attached files, and retrieved pages.\n3. \n**Choose a filter that understands the format.** Start with deterministic rules for known formats, such as pytest output or JSON, and pass unfamiliar input through unchanged.\n4. \n**Check before replacing.** Make sure required error details, file paths, numbers, and instructions survive, and fall back to the previous checked version or the original if they don't.\n5. \n**Measure the full task.** Count hook notes, skill instructions, references to the original, and rejected rewrite attempts, then check the complete model requests and whether the agent still finishes the job correctly. Keep an eye on caching when changing text reused across requests.\n\nTry the same task again with the smaller input and check that the agent can still find the failure and produce a working fix. Once that holds up across more inputs, you can expand the rules and decide whether any remaining text needs a model to compress it.\n\n## Follow me\n\nIf you found this blog post helpful, don't forget to share it with your network. For more content on AI and web development, subscribe to my [YouTube channel](https://www.youtube.com/@gethackteam?sub_confirmation=1) and connect with me on [LinkedIn](https://linkedin.com/in/gethackteam), [X](https://x.com/gethackteam), or [Bluesky](https://bsky.app/profile/gethackteam.bsky.social).", "url": "https://wpnews.pro/news/compression-should-be-part-of-your-agent-harness", "canonical_source": "https://hackteam.io/blog/why-compression-should-be-part-of-your-agent-harness/", "published_at": "2026-10-21 13:00:51+00:00", "updated_at": "2026-10-21 13:12:44.459140+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "large-language-models", "developer-tools"], "entities": ["RTK", "Caveman", "Ponytail", "Claude", "Cursor", "Codex"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/compression-should-be-part-of-your-agent-harness", "markdown": "https://wpnews.pro/news/compression-should-be-part-of-your-agent-harness.md", "text": "https://wpnews.pro/news/compression-should-be-part-of-your-agent-harness.txt", "jsonld": "https://wpnews.pro/news/compression-should-be-part-of-your-agent-harness.jsonld"}}