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AI coding agents can resolve real-world software issues, yet they frequently introduce regressions, breaking tests that previously passed. Current benchmarks focus almost exclusively on resolution rate, leaving regression behavior under-studied. This paper presents TDAD (Test-Driven Agentic Development), an open-source tool and benchmark methodology that combines abstract-syntax-tree (AST) based code-test graph construction with weighted impact analysis to surface the tests most likely affected by a proposed change. Evaluated on SWE-bench Verified with two local models (Qwen3-Coder 30B on 100 instances and Qwen3.5-35B-A3B on 25 instances), TDAD's GraphRAG workflow reduced test-level regressions by 70% (6.08% to 1.82%) and improved resolution from 24% to 32% when deployed as an agent skill. A surprising finding is that TDD prompting alone increased regressions (9.94%), revealing that smaller models benefit more from contextual information (which tests to verify) than from procedural instructions (how to do TDD). An autonomous auto-improvement loop raised resolution from 12% to 60% on a 10-instance subset with 0% regression. These findings suggest that for AI agent tool design, surfacing contextual information outperforms prescribing procedural workflows. All code, data, and logs are publicly available at this https URL.