Dual-Process Agent (DPA)
https://www.mdpi.com/2079-9292/15/6/1232
System 1: fast retrieval path that pulls compressed, relevant memories and conditions generation. System 2: slow reflection pass that evaluates outcomes and writes curated updates back through a conservator gate. No model-weight changes.
At a glance
- Type
- Kahneman System 1 / System 2
- Tier
- T3
- Created
- 2026-03-16 (MDPI Electronics 15(6) 1232; published March 16 2026)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- not applicable — not commercial
- Funding
- not applicable — not commercial
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- consolidation
- unit
- summary
- governance
- inspectable
- conflict
- llm-arbitrate
When to use
Optimised for: not applicable - research paper
Anti-fit: not applicable - research paper
Pros & cons
Pros
System-1 / System-2 dual-process model applied to LLM agents.
Cons
Dual-process framing is descriptive; production tradeoffs against single-loop agents are unclear.
Claims & capabilities
Outperforms vanilla prompting on six benchmarks with both GPT and Llama backbones. MDPI Electronics 2025.
Technical surface
- API surface
- not applicable — research paper
- Backend storage
- not applicable — research paper
- Deployment
- not applicable — not a deployable product
- Embedding model
- not applicable — research paper
- Multi-tenancy
- not applicable — research paper
- MCP
- not applicable — research paper, no deployed product
- A2A
- not applicable — research paper, no deployed product
- OpenTelemetry
- not applicable — research paper, no deployed product
Similar systems
Other recent method papers — theorized, no distinct product in the catalog, ranked by inbound references.
- Compressive Transformer T3
Maintains recent states in full resolution while compressing older memories with learned compression functions. DeepMind.
- MemGPT v2 / agent-tools T3
Already in catalog as the foundational MemGPT paper. Note: Letta is the productionised successor (cross-listed).
- Transformer-XL T3
Extends context through segment-level recurrence + caching of hidden states from prior segments. Foundational long-context architecture.
- Generative Agents T3
Park et al. — landmark agent-simulation paper. Reflection + memory stream + retrieval enable believable agent behavior.
- MemoryBank T3
Enhances LLMs with long-term memory. Early influential paper.
- Reflexion T3
Language agents with verbal reinforcement learning.