Microsoft Research Introduces CORPGEN To Manage Multi Horizon Tasks For Autonomous AI Agents Using Hierarchical Planning and Memory

Why It Matters

By enabling reliable, multi‑task autonomous agents, CORPGEN can reduce human supervision and accelerate AI‑driven automation across enterprise workflows, reshaping productivity benchmarks.

Key Takeaways

• Defines Multi-Horizon Task Environments for enterprise AI.
• Hierarchical planning splits goals into strategic, tactical, operational layers.
• Tiered memory prevents cross‑task interference and token overflow.
• Experiential learning yields up to 3.5× higher completion rates.
• Benchmarks underestimate agents by focusing on trace‑based evaluation.

Pulse Analysis

Enterprises increasingly rely on autonomous software agents to handle routine paperwork, data entry, and customer interactions. Traditional evaluations, however, treat each task as an isolated episode, ignoring the tangled web of concurrent responsibilities that real corporate workflows present. Microsoft researchers label this gap as Multi‑Horizon Task Environments (MHTEs), where dozens of interdependent tasks compete for limited context windows and computational bandwidth. In MHTEs, agents must juggle long‑term objectives, dynamic dependencies, and priority shifts—challenges that expose severe performance drops in conventional computer‑using agents.

CORPGEN tackles MHTEs with a four‑pronged architecture. Hierarchical planning decomposes goals into monthly strategic objectives, daily tactical plans, and per‑cycle operational actions, preserving coherence across time scales. Sub‑agent isolation assigns each complex tool interaction to a dedicated micro‑agent, shielding the main context from cross‑task contamination. A tiered memory stack—working, structured long‑term, and semantic—stores immediate reasoning, typed artifacts, and embedding‑based retrievals, respectively, keeping token usage linear. Finally, adaptive summarization compresses routine reasoning once the context exceeds 4,000 tokens, ensuring the model stays within its window while retaining critical state changes.

Empirical tests on three CUA backends show CORPGEN delivering up to 3.5× higher task‑completion rates, rising from 4.3 % to 15.2 % under full load. Ablation experiments pinpoint experiential learning—reusing successful execution trajectories—as the dominant gain driver. Moreover, the study reveals that artifact‑based evaluation aligns with human judgment far better than trace‑based methods, suggesting current benchmarks may undervalue agent capabilities. For businesses, these advances mean autonomous agents can reliably manage dense, interlinked workloads, reducing manual oversight and accelerating digital transformation across finance, HR, and operations.