This topic covers the development, evaluation, and governance of AI-driven suspect‑sketch and forensic‑image tools, focusing on accuracy, bias, ethics, and vendor comparisons. By 2026 these systems are increasingly used in investigations and intelligence workflows, raising timely questions about misidentification risk, demographic performance disparities, chain‑of‑custody, and regulatory compliance. Practical deployment now requires not only high fidelity image synthesis and matching but also demonstrable provenance, explainability, and auditability. Key tooling and capabilities include enterprise LLM platforms and agent frameworks used to build front‑end workflows (IBM watsonx Assistant), no‑code/low‑code design and operationalization environments for rapid agent deployment (MindStudio), engineering frameworks for building and testing reliable agentic applications and evaluation harnesses (LangChain), conversational and analytic LLMs for human‑machine interaction and case summarization (Anthropic’s Claude family), and infrastructure/optimization traces such as vendor consolidations and performance audits (Deci.ai/NVIDIA observations). These tools form components of pipelines for suspect‑sketch generation, image enhancement, matching, documentation, and operator review. Relevant trends include stronger regulatory scrutiny of forensic AI, demand for standardized independent benchmarks (accuracy, false‑positive/negative rates, cross‑demographic fairness), requirements for tamper‑evident provenance and metadata for chain‑of‑custody, and increasing attention to adversarial robustness and deepfake detection. For evaluations and vendor comparisons, prioritize transparent metrics, third‑party testing, human‑in‑loop safeguards, and integration with AI governance and compliance tooling. The goal is practical, auditable deployments that minimize harms while supporting lawful investigative uses.