A research-backed framework for deploying intelligent AI agents into your organization. Seven critical dimensions for successful agentic AI implementation.
We evaluated OpenClaw, LangChain, and a bespoke Python stack — all running Siggy, the TeamSpec reference implementation — across 7 dimensions. The radar shows where each platform leads and where it falls short. Scores reflect actual platform behavior, not marketing claims.
Each polygon represents one platform's score across all 7 axes. Red (OpenClaw) leads on authority management and trust by design — it was built to the spec. Green (LangChain) leads on failure handling and scalability — the advantage of a mature ecosystem. Amber (Bespoke Python) shows the ceiling of a single-user personal stack when measured against enterprise compliance requirements. Framework draws from recent research on intelligent AI delegation.
Abstract platform comparisons produce abstract scores. We evaluated all three platforms against an identical, fully-specified agent configuration — Siggy, an executive AI assistant — running the same five task scenarios on each.
Siggy is an executive AI assistant capable of administering calendars, setting meetings, researching topics and prospects, sending messages, analyzing opportunities, analyzing impediments, and delegating tasks to both other agents and humans. It is a production-grade agent specification, not a demo — complex enough to exercise every dimension of the TeamSpec standard under realistic conditions.
Five task scenarios tested each dimension: a multi-step meeting briefing, an authority-gated message dispatch, a multi-agent delegation workflow, a live API failure during scheduling, and a complete weekly action audit. The full case study includes detailed findings for each platform on each task, the honest strengths and limitations of each, and guidance on which platform fits which use case.
Can your agent break complex goals into meaningful sub-tasks? Does it dynamically adapt when conditions change? We evaluate decomposition sophistication and dynamic adaptation capabilities.
Clear authority transfer, accountability tracking, and well-defined role boundaries. Who has decision-making power? Who's responsible for each outcome? Are agent capabilities and limitations explicit?
Verification systems for task completion, reputation tracking for agent reliability, and safety guardrails to prevent harmful actions. This is the most critical dimension for enterprise deployment.
Does your agent notice when things go wrong? Can it adapt or rollback? Can it request human help appropriately? Robust error detection, recovery strategies, and escalation paths are essential.
Can users clearly state what they want? Do agents communicate their reasoning? Clear goal specification and transparent agent communication prevent misalignment and wasted effort.
Can your system handle multiple agents and delegation chains? Does it efficiently distribute work? Network scalability and resource management become critical as you grow.
Can users see what's happening? Does the agent explain its choices? Execution visibility and decision explainability build trust and enable debugging when things go wrong.
We evaluate platforms and applications across all seven dimensions on a 100-point scale
We score and compare agentic AI platforms (LangChain, AutoGen, CrewAI, etc.) against all seven dimensions to find the right fit for your use case.
Step-by-step deployment plan tailored to your organization—from pilot projects to production rollout across all seven dimensions.
Trust mechanisms, verification systems, and safety guardrails specifically designed for your industry and compliance requirements.
Dashboard templates and logging strategies to track agent performance, debug failures, and maintain transparency.
Error detection systems, recovery strategies, and human escalation workflows to ensure resilience in production.
Train your engineers and stakeholders on intelligent delegation principles, best practices, and how to evaluate agent performance.
Get a custom playbook based on proven research and battle-tested frameworks.