Papers › Automated Design of Agentic Systems

Automated Design of Agentic Systems

15 Aug 2024arXiv:2408.08435archive 2025-07-28

Shengran Hu, Cong Lu, Jeff Clune

Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries. Through extensive experiments across multiple domains including coding, science, and math, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2408.08435")

Code

Syntology Ran 10 of 13 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 10 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 10 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

shengranhu/adas officialmentioned in papermentioned on GitHubApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

13 samples harvested; 10 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

10ran
3unverified

Licence: 0 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from shengranhu/adas. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

answer_json_to_string shengranhu/adas/_drop/utils.py official repository ran Apache-2.0 (permissive) · f08515656f31c846 · report
answer_json_to_strings shengranhu/adas/_drop/utils.py official repository ran Apache-2.0 (permissive) · b8b06489c92899f0 · report
bootstrap_confidence_interval shengranhu/adas/_gpqa/utils.py official repository ran Apache-2.0 (permissive) · 981c8f72d0181b71 · report
file_to_string shengranhu/adas/_arc/utils.py official repository ran Apache-2.0 (permissive) · 765cc65c8097e240 · report
get_drop_metrics shengranhu/adas/_drop/utils.py official repository ran Apache-2.0 (permissive) · d3d85aa9528f0ea0 · report
get_reflexion_prompt shengranhu/adas/_arc/arc_prompt.py official repository ran Apache-2.0 (permissive) · 3386b2b248cf7d4b · report
get_reflexion_prompt shengranhu/adas/_drop/drop_prompt.py official repository ran Apache-2.0 (permissive) · 03e103c0289af528 · report
list_to_string shengranhu/adas/_arc/utils.py official repository ran Apache-2.0 (permissive) · 223b46a8f9051398 · report
load_questions shengranhu/adas/_gpqa/utils.py official repository ran Apache-2.0 (permissive) · 7b1e70edd9eaffe7 · report
random_id shengranhu/adas/_arc/utils.py official repository ran fingerprinted Apache-2.0 (permissive) · 4488e68c352c9d58 · report
get_prompt shengranhu/adas/_arc/arc_prompt.py official repository unverified Apache-2.0 (permissive) · e303009857c65536 · report
get_prompt shengranhu/adas/_drop/drop_prompt.py official repository unverified Apache-2.0 (permissive) · 7c697edb0437e5f5 · report
get_prompt shengranhu/adas/_gpqa/gpqa_prompt.py official repository unverified Apache-2.0 (permissive) · 505fd0fb8ba0fca7 · report

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections