pith:D3EHK3FA
FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing
A single agent can design complete agentic workflows end-to-end by making sequential edits to an executable canvas that supplies real-time syntax-checked feedback.
arxiv:2602.01664 v4 · 2026-02-02 · cs.AI · cs.LG
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\pithnumber{D3EHK3FAKT2IFQVUJBTQJC45WK}
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Record completeness
Claims
Experimental results on twelve datasets show that FlowSteer significantly outperforms baselines across various tasks.
That real-time syntax-checked execution feedback from the Workflow Canvas is sufficient to train a policy agent that can reliably repair errors during long-horizon workflow construction without external human guidance.
A reinforcement learning policy agent designs executable agentic workflows by issuing atomic edits to a feedback-providing Workflow Canvas environment.
References
Receipt and verification
| First computed | 2026-05-17T23:39:16.456876Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1ec8756ca054f482c2b44867048b9db2b3ff2c6935893b22ff762378c381dacc
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/D3EHK3FAKT2IFQVUJBTQJC45WK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1ec8756ca054f482c2b44867048b9db2b3ff2c6935893b22ff762378c381dacc
Canonical record JSON
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