We need re-learn what AI agent development tools are in 2026

6 hours ago

The AI agent market in 2025 saw consensus on agent behavior and techniques like sub-agents to bypass context windows.
Capabilities such as RAG, memory, tools, and evaluations have become commoditized, with many now natively available in LLM services.
MCP security strategy was appropriated by OpenClaw, but its vulnerabilities make it unsuitable for sensible organizations.
The evaluation framework for AI agent builders needs updates, focusing on commoditized features, standing capabilities, and underappreciated aspects.
Key commoditized features include document uploads, connectors, and prompt templates in services like ChatGPT and Claude.
Codability axis remains important, with routing, parallelization, orchestrator-workers, sequential agents, and multi-agents as key evaluation points.
Deterministic logic in agents is underappreciated, crucial for ensuring predefined processes in tasks like security operations.
Evaluation changes include dropping the integrability axis, refining triggers, and adding an enterprise-readiness axis for responsible deployments.
Vendors have evolved with enterprise-grade functions, funding, acquisitions, and certifications, while large LLM providers entered the visual no-code development space.
Coding agents are targeted at developers, with a focus on LLM-generated code within workflows rather than evaluating application writing abilities.

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