Which of the Three Types of AI Agents Are You Building?

AGI Roadmap Layers

The five‑layer structure consists of Conversation (C), Reasoning (R), Agent (A), Innovation (I) and Organization (O). Layer 4 adds multimodal, multi‑agent capabilities together with reinforcement learning (RL), chain‑of‑thought (CoT) and simulation. Layer 5 adds safety‑boundary control, advanced alignment, scalable orchestration and super‑long‑memory supervision. The author notes that predictions made before 2025 underestimated the speed at which L4 and L5 features merged into the current agent era.

Three Dominant Agent Families

Foundation‑model RL agents – exemplified by DeepSeek V4. They combine multimodal mixture‑of‑experts (MoE), OPD‑based multi‑agent RL and the DSec Sandbox for task simulation.

OpenClaw‑style agents – built on a hierarchy of Skills, Memory and Orchestration. The OpenClaw “40 Questions” discussion describes their evolution.

Ontology‑driven agents – represented by Palantir’s Ontology Agent, which uses an ontology as the glue for physical‑world digital twins, enforcing safety and compliance.

1. Foundation‑model RL Agents (LLM‑Alpha‑Zero)

These agents extend the AlphaZero paradigm to large language models:

Data source : AlphaZero trains in a game‑playing sandbox. In the LLM context, DS R1 generates data via CoT, while DS V4 provides an Agent Track inside the DSec Sandbox.

Sparse convergence optimization : AlphaZero relies on multi‑step look‑ahead. DS R1 applies GRPO; DS V4 uses MT‑OPD.

Reward mechanism : AlphaZero optimizes final game outcomes. DS R1 uses RLVR + LLM‑as‑Judge; DS V4 combines RLHF + RLAIF with an Agent‑as‑Judge.

Emergent capability through scaling : AlphaZero produces “aha moments”. DS R1 seeks sudden insights; DS V4 focuses on task‑level decomposition.

Scaling data, optimization and reward components is intended to produce emergent abilities comparable to AlphaZero’s foresight.

2. OpenClaw Autonomous Agents (Skill → Harness → Hermes)

OpenClaw agents emphasize context engineering, skill orchestration and long‑term memory:

OpenClaw Agent = Lazy‑Context (Skill + Memory) + Plan‑Act‑Observe.

Harness Agent = Context Engineer (Skill + Memory + Orchestration + Tools + Verification + Constraint).

Hermes Agent = Lazy‑Context + Plan‑Act‑Observe‑Learn.

Compared with earlier autonomous agents, OpenClaw shows substantial improvements in task decomposition, orchestration and super‑long memory.

3. Ontology‑Enabled Agents

Palantir’s Ontology Agent places an ontology at the core of the system. The ontology defines physical‑world constraints; only actions satisfying these constraints are permitted, guaranteeing safety, determinism and high compliance. The agent can query billions of business objects and orchestrate thousands of actions under the same governance framework as human employees. The author cites the “Anthropic and Palantir collaboration” discussion, noting that current ontology construction is costly and talent‑intensive, motivating the search for scalable approaches.

Interaction and Convergence

The three families influence each other. Foundation‑model agents supply a data‑rich RL backbone, OpenClaw agents contribute sophisticated orchestration and memory mechanisms, and ontology‑driven agents provide safety and real‑world grounding. By 2026 the author expects full integration, enabling AI‑native companies, hybrid human‑machine organizations and decentralized AGI networks (DAO + Agent).

Future Outlook

RL + Ontology is identified as a new hotspot, with an “Agent OS” envisioned as the infrastructure for hybrid organizations. Early explorations such as LLM Wiki illustrate the direction, but full integration of LLM, Ontology, RL and Agents remains a longer‑term goal.

Key Technical Comparisons

AlphaZero vs. LLM‑Alpha‑Zero: data shifts from game simulations to CoT‑generated corpora; optimization shifts from pure look‑ahead to GRPO/MT‑OPD; reward shifts from final outcome to RLVR/LLM‑as‑Judge or RLHF + RLAIF + Agent‑as‑Judge.

OpenClaw vs. earlier autonomous agents: addition of explicit Skill, Memory, Orchestration, Tool use, verification and constraint layers improves task decomposition and long‑term recall.

Ontology‑driven vs. sandbox‑only agents: ontology provides a deterministic safety layer that grounds actions in physical‑world constraints, reducing simulation cost for digital twins.

Representative Images