3.3 (3.1) Agent PAL core demos

← 3 Agents

PAL_CORE demos focus on the core deterministic functions of Palantir-style agentic systems. Unlike many AI demos, the emphasis is on traditional software control loops that perform detection, prediction, allocation, planning, and optimization. AI is added only where it provides a clear benefit.

PAL_CORE demos 3.3.4, 3.3.5, 3.3.8, and 3.3.8b use OpenAI models.

For details see

• lab notes doc #603_PAL_.docx (PAL = Palantir) on the Gdrive.

TOC

• 3.3.1 pal_core_01.detect.py (01 events / alerts) (BINGO) 26.0411. Demonstrates event detection and alert generation. Serves as the simplest “sensor → detection → alert” workflow.
• 3.3.2 pal_core_02_predict.py (02 road network / prediction) (BINGO) 26.0411. Demonstrates prediction using a simplified road-network model. Shows how future conditions can be estimated from current observations.
• 3.3.3 pal_core_03_allocate.py (03 resources / allocation) (BINGO) 26.0411. Demonstrates deterministic resource allocation. Assigns available resources to tasks using predefined rules and priorities.
• 3.3.4 pal_core_04 (graph/relationships) (BINGO) 26.0412. Demonstrates relationship analysis using graph structures. Shows how entities and connections can be explored and queried.
• 3.3.5 pal_core_05_operate.py (combine core01-04) (BINGO) 26.0415-16. Combines detection, prediction, allocation, and graph analysis into a single operational workflow. Represents the first complete PAL_CORE system.
• 3.3.5b bcd_pal_core_05 #592 “LLM bcd”. Early experiment exploring where an LLM can assist within the PAL_CORE workflow while maintaining deterministic execution.
• 3.3.6 3.3.6_pal_core_06_all_roles (simplest demo of all core LLM roles) 26.0416. Demonstrates the major roles an LLM can play in an agentic system, including classification, extraction, summarization, planning, and explanation.
• 3.3.7 pal_core_07_gmail_alerts.py (real emails as sensor stream) (BINGO) 25.0423. Uses incoming Gmail messages as a real-world event stream. Demonstrates monitoring, filtering, and alert generation using live data.
• 3.3.8 pal_core_08_plan.py (natural language → plan JSON → controlled execution) (BINGO) 26.0423. Converts natural-language requests into structured plans and executes them through controlled deterministic workflows.
• 3.3.8b pal_core_08_plan.py + LLM (BINGO) 26.0424. Extends the planning demo by using an LLM to generate plans while retaining deterministic validation and execution.
• 3.3.9 pal_core_09_optimize.py (improved allocation / optimization) (BINGO) 26.0427. Improves the allocation process by evaluating multiple possible assignments and selecting better solutions.
• 3.3.10 (todo) pal_core_10_ui.py // dashboard / FastAPI / presentation layer 26.0427. Planned dashboard and presentation layer. Intended to provide a FastAPI-based user interface for interacting with PAL_CORE services.

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