Designing Intelligent Enterprise Agents: A Capability-Aligned Multi-Agent Architecture
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Enterprise interest in multi-agent systems has shifted from generic software agents to large-language-model (LLM) based intelligent agents that plan, use tools, maintain contextual memory, inspect intermediate results, collaborate with other agents, and sometimes act in systems of record. This paper revises the enterprise architecture thesis around a design-first claim: governance is necessary, but it cannot be the primary organizing abstraction. The primary abstraction must be agent design - capability boundaries, autonomy allocation, interaction protocols, tool and data authority, state and memory design, verification design, and human interaction design. We propose CEAD (Capability-Aligned Enterprise Agent Design), a reference architecture for intelligent agents that uses service-oriented architecture (SOA) as an exemplar for contracts, registries, loose coupling, and policy-aware integration, while explicitly rejecting the idea that services are agents. It treats microservices as a cautionary precedent: decomposition without design discipline produces distributed complexity, cost, operational fragility, and agent proliferation. We evaluate CEAD over 10,000 enterprise tasks, comparing five architectures: a prompt-first mono-agent, a role-based micro-agent swarm, SOA-brokered agents, a governance-first but design-poor agent grid, and the proposed CEAD architecture. CEAD achieves 70.6% safe success, versus 45.2% for the mono-agent baseline, 23.1% for the ungoverned micro-agent swarm, 58.8% for SOA-brokered agents, and 50.8% for the control-heavy, design-poor grid. The results support the conclusion that design quality is the first-order enterprise concern; governance, security, policy, audit, and assurance should support and enforce good design rather than substitute for it.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 11 of its 51 references that are in the map. First posted on arXiv 7 May 2026.
Builds on
11 of 51 references.
Closest ancestors · 3
Same direction, newest first
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges21 Jan
- 2023A Survey on Large Language Model based Autonomous Agents22 Aug
- 2023AgentBench: Evaluating LLMs as Agents7 Aug
Other references · 8
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2023GAIA: a benchmark for General AI Assistantscited by 1,128
- 2023Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Modelscited by 264
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