Agentic Systems Research: Q1 2026
Table of Contents
Overview
Research areas in agentic AI systems for Q1 2026, focusing on gaps in current frameworks and emerging standards.
Team Planning Document: Q1 2026 Research Plan
Problem Domains
Agent Identity and Governance
Current multi-agent frameworks (LangGraph, CrewAI, AutoGen) focus on orchestration but lack principled approaches to:
- Attribution: Who authorized this agent? Who is accountable for its actions?
- Ontology: What IS this agent? Why does it behave this way?
- Governance: How do we audit, constrain, and trust autonomous agents?
Related Research
- Constitutional AI (Anthropic) - AI systems with explicit behavioral constraints
- Sparks of AGI (Microsoft) - Emergent behaviors in large models
- Many-shot jailbreaking - Safety implications of in-context learning
- Role-based agent architectures in CrewAI (Manager/Worker/Researcher patterns)
Multi-Agent Orchestration
Coordination patterns for multiple AI agents working together on complex tasks.
Current Approaches
| Framework | Pattern | Strengths |
|---|---|---|
| LangGraph | State graphs, supervisor | Fine-grained control, lowest latency |
| CrewAI | Role-based crews | Simple setup, 60% Fortune 500 adoption |
| AutoGen | Conversational | Flexible, now Microsoft Agent Framework |
Open Problems
- Conflict resolution: When agents disagree, how to arbitrate?
- Resource contention: Multiple agents competing for expensive models
- Emergent behavior: Unintended coordination patterns at scale
- Adversarial review: Structured challenge/response between agents
Related Research
- MetaGPT - Multi-agent software development with SOP
- Communicative Agents for Software Development (CAMEL)
- AutoGen to Microsoft Agent Framework evolution
- AWS Bedrock + LangGraph multi-agent patterns
Protocol and Communication Standards
Standardized interfaces for agent-to-agent and agent-to-tool communication.
Model Context Protocol (MCP)
Anthropic's open standard for AI-tool integration, November 2025 spec:
- Async operations and statelessness
- Server identity verification
- Official extensions framework
- 97M+ monthly SDK downloads
December 2025: Donated to Agentic AI Foundation (Linux Foundation)
Agent-to-Agent (A2A)
Google's protocol for inter-agent communication, complementing MCP.
Security Concerns
April 2025 research identified:
- Prompt injection via tool outputs
- Permission escalation through tool combinations
- Lookalike tools silently replacing trusted ones
Related Research
- MCP November 2025 Specification
- Why the Model Context Protocol Won (The New Stack)
- MCP Architecture Overview (Descope)
Evaluation and Decision Frameworks
Measuring agent performance beyond simple accuracy metrics.
Key Benchmarks
| Benchmark | Focus | Challenge Level |
|---|---|---|
| AgentBench | 8 interactive environments | Planning, reasoning |
| GAIA | 466 real-world tasks | Multimodality, tools |
| WebArena | 812 web tasks | Functional correctness |
| OSWorld/AppWorld | Expert skills | Best agents score ~5% |
Enterprise Framework: CLASSic (ICLR 2025)
Five dimensions for production AI agents:
- Cost: API usage, token consumption
- Latency: End-to-end response times
- Accuracy: Workflow execution correctness
- Stability: Consistency across inputs
- Security: Resilience against adversarial inputs
Gaps in Current Evaluation
- Cost-efficiency rarely measured alongside accuracy
- Safety and robustness underexplored
- No standard for measuring agent coordination quality
- Limited evaluation of long-horizon tasks
Related Research
- Survey on Evaluation of LLM-based Agents (March 2025)
- Future of AI Agent Evaluation (IBM Research)
- 10 AI Agent Benchmarks (Evidently AI)
- Enterprise Agent Benchmarks (Aisera)
Agent Economics and Resource Allocation
Token-based systems for coordinating agent resource usage.
Problem Space
- Resource allocation: How do agents access expensive models (Claude Opus)?
- Incentive alignment: How to reward productive work, discourage waste?
- Collaboration funding: How do multiple agents pool resources for expensive operations?
- Rate limiting: How to prevent runaway spending?
Industry Approaches
Decentralized AI Networks
| Project | Mechanism | Market Cap (Jan 2025) |
|---|---|---|
| ASI Alliance | Merged Fetch.ai + SingularityNET + Ocean | $9.2B |
| Bittensor (TAO) | Decentralized ML marketplace | Active |
| ai16z | Solana-based, Eliza framework | $2B |
Pricing Models
2025 trend: Usage-based/token-based pricing replacing flat subscriptions
- Microtransactions (pay-per-task)
- Token economies with two-sided incentives
- Staking for compute access
Proposed Frameworks
- Agent Exchange (AEX) - RTB-inspired auction system for agent services
- User-Side Platform (USP) / Agent-Side Platform (ASP) architecture
- Data Management Platforms for agent capability matching
Framework Comparison
| Concern | LangGraph | CrewAI | AutoGen/MAF | Gap |
|---|---|---|---|---|
| Identity | - | - | - | No attribution chain |
| Governance | - | - | Filters | No audit trail |
| Adversarial review | - | - | - | No structured challenge |
| Cost tracking | - | - | Partial | No economy model |
| Evaluation | External | External | External | No built-in metrics |
Q1 2026 Research Questions
- How to create verifiable identity chains from human principals to AI agents?
- What ontological categories best describe agent capabilities and constraints?
- How should adversarial review be structured for multi-agent systems?
- What economic models align agent incentives with user goals?
- How to evaluate agent coordination quality, not just individual performance?
References
Frameworks
- LangGraph - State machine orchestration
- CrewAI - Role-based multi-agent
- Microsoft Agent Framework - AutoGen + Semantic Kernel
Standards
- Model Context Protocol - Tool integration standard
- Agentic AI Foundation - MCP governance