How to choose an AI Agent platform for your team
2 days ago
- An estimated 88% of AI agent pilots fail to reach production, with scope creep (34%) and data quality issues (27%) as the leading causes.
- Six evaluation criteria separate a platform that works in a demo from one that works in production: tool orchestration, human-in-the-loop controls, audit trails, connector coverage via MCP, persistent and correctable memory, and transparent pricing with real usage limits.
- Governance pressure from the EU AI Act (Article 14) makes human oversight and inspectable activity compliance requirements, not just preferences.
- Many vendors engage in 'agent washing' – relabeling chatbots or RPA as agents – so the evaluation criteria focus on actual capabilities.
- A 6-point scorecard is provided to compare platforms: execution surfaces, human-in-the-loop, audit trail type, connector extension path, memory model, and pricing/limits clarity.
- Construct's platform is used as a running example, with explicit acknowledgment of its boundaries (e.g., bounded audit summaries, linear workflows, no mandatory approval gates).