Practical LLM Agents & Budgeted Reasoning
Key Questions
What approaches dominate practical LLM agent development?
Hybrid solver plus agent stacks and budget-aware planners remain the main practical methods, as seen in ADC-LLM and budgeted value-tree search. These shift focus from brute-force prompting toward verifiable multi-turn tool use.
How are verification-focused agents like MiroThinker improving LLM workflows?
Research agents from MiroMind emphasize verifiable processes, while tools like TRUST-SQL enhance grounding. This moves work away from prompting toward reliable, multi-turn interactions.
What unknowns exist around enterprise LLM agent deployments?
Key uncertainties include latency and cost tradeoffs, ADC-LLM scaling claims, and the lack of robust sim-to-real evaluations for long-horizon tasks. Enterprise demos from Microsoft and others remain product-focused rather than peer-reviewed.
Hybrid solver + agent stacks and budget‑aware planners remain the dominant practical approach (ADC‑LLM, budgeted value‑tree search, EP122 pillars). Verification‑focused research agents from MiroMind (MiroThinker‑1.7 / H1) and improved tool grounding (TRUST‑SQL) are shifting work from brute‑force prompting to verifiable multi‑turn tool use. Enterprise demos (Microsoft 365 Copilot SharePoint, MuleSoft Agent Fabric, Capy.ai's captain/build write‑ups) show deployment patterns but are product‑centric rather than peer‑reviewed research. Key unknowns: latency/cost tradeoffs, ADC‑LLM scaling claims, and robust sim→real long‑horizon evaluation.