Researchers have successfully developed and evaluated MIRA, an autonomous artificial intelligence agent designed to navigate complex emergency department workflows within a sandboxed electronic health record environment. The system addresses a critical gap in medical AI by moving beyond isolated diagnostic tasks to simulate end-to-end clinical decision-making, integrating directly with Fast Healthcare Interoperability Resources standards to interact with realistic hospital software architectures. The evaluation utilized a benchmark of 574 patient cases drawn from the MIMIC-IV dataset, spanning eight high-acuity conditions including appendicitis, pneumonia, pulmonary embolism, and pancreatic cancer. MIRA operates through a conversational framework, engaging with a simulated patient agent grounded in documented medical histories while utilizing over 85,000 clinical decision options. These options encompass ordering laboratory and imaging tests, interpreting results, prescribing medications, scheduling procedures, and managing admissions, all while adhering to six major medical coding standards. Performance was rigorously benchmarked against two independent physician cohorts: board-certified specialists and a mixed-seniority team reflecting typical emergency department staffing. Diagnostic testing revealed that MIRA achieved an average accuracy of 88.9 percent, consistently matching or exceeding human performance across nearly all evaluated pathologies. The AI agent mirrored clinical reasoning by following stepwise workflows from initial triage through diagnostic testing to treatment initiation. Notably, MIRA demonstrated superior guideline adherence for medication prescribing, outperforming physicians by an average margin of 35 percentage points in targeted therapeutic categories. Procedural recall also showed strong alignment with clinical benchmarks, particularly in surgical interventions for appendicitis and cholecystitis, where the agent correctly identified relevant procedures at rates significantly higher than human comparators. Safety and robustness assessments further validated the system's clinical viability. A blinded physician review found zero instances of high-severity drug interactions, renal dosing incompatibilities, or unsafe opioid prescriptions. Medication reconciliation achieved 95.2 percent recall and 99.6 percent precision, with 99.8 percent of prescriptions containing clinically accurate dosing instructions. In disposition trials focusing on admission criteria, MIRA maintained perfect recall for identifying patients requiring hospitalization, demonstrating a conservative but safe clinical posture. The agent also exhibited stability when exposed to demographic and cognitive bias perturbations, with performance fluctuations remaining within statistically negligible margins. Despite these advances, researchers acknowledge inherent limitations. The simulation relies on structured discharge summaries rather than real-time patient dialogue, potentially underrepresenting clinical disfluency. Furthermore, the sandboxed evaluation reflects controlled conditions rather than live clinical environments. The authors emphasize that MIRA is not intended to replace healthcare professionals but to function as a collaborative clinical copilot, automating documentation-heavy tasks and providing evidence-based recommendations under human supervision. Future development will prioritize prospective real-world validation, enhanced resource stewardship mechanisms to prevent diagnostic overtesting, and iterative refinement. The framework establishes a new benchmark for agentic AI in healthcare, paving the way for standards-compliant, workflow-integrated medical assistants.