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| # Agents System Design | |
| ## Overview | |
| The agent runtime is a multi-agent, memory-aware RL orchestration layer for web extraction tasks. It supports: | |
| - Single-agent and multi-agent execution modes | |
| - Strategy selection (`search-first`, `direct-extraction`, `multi-hop-reasoning`) | |
| - Human-in-the-loop intervention | |
| - Explainable decision traces | |
| - Self-improvement from past episodes | |
| ## Agent Roles | |
| ### 1. Planner Agent | |
| Builds a plan before action: | |
| - Goal decomposition | |
| - Tool selection plan | |
| - Risk and fallback path | |
| ### 2. Navigator Agent | |
| Explores pages and search results: | |
| - URL prioritization | |
| - Link traversal policy | |
| - Page relevance scoring | |
| ### 3. Extractor Agent | |
| Extracts structured fields: | |
| - Selector and schema inference | |
| - Adaptive chunk extraction | |
| - Long-page batch processing | |
| ### 4. Verifier Agent | |
| Checks consistency and trust: | |
| - Cross-source verification | |
| - Conflict resolution | |
| - Confidence calibration | |
| ### 5. Memory Agent | |
| Manages memory write/read/search: | |
| - Episode summaries | |
| - Pattern persistence | |
| - Retrieval ranking and pruning | |
| ## Execution Modes | |
| ### Single-Agent | |
| One policy handles all actions. | |
| Pros: low overhead, simple. | |
| Cons: weaker specialization. | |
| ### Multi-Agent | |
| Coordinator delegates work: | |
| 1. Planner emits execution graph | |
| 2. Navigator discovers candidate pages | |
| 3. Extractor parses and emits data | |
| 4. Verifier validates outputs | |
| 5. Memory Agent stores reusable patterns | |
| Pros: modular, robust, scalable. | |
| Cons: coordination overhead. | |
| ## Agent Communication | |
| Shared channels: | |
| - `agent_messages`: async inter-agent messages | |
| - `task_state`: current objective and progress | |
| - `global_knowledge`: reusable facts and patterns | |
| Message schema: | |
| ```json | |
| { | |
| "message_id": "msg_123", | |
| "from": "navigator", | |
| "to": "extractor", | |
| "type": "page_candidate", | |
| "payload": { | |
| "url": "https://site.com/p/123", | |
| "relevance": 0.91 | |
| }, | |
| "timestamp": "2026-03-27T00:00:00Z" | |
| } | |
| ``` | |
| ## Decision Policy | |
| Policy input includes: | |
| - Observation | |
| - Working memory context | |
| - Retrieved long-term memory hits | |
| - Tool registry availability | |
| - Budget and constraints | |
| Policy output includes: | |
| - Next action | |
| - Confidence | |
| - Rationale | |
| - Fallback action (optional) | |
| ## Strategy Library | |
| Built-in strategy templates: | |
| - `search-first`: broad discovery then narrow extraction | |
| - `direct-extraction`: immediate field extraction from target page | |
| - `multi-hop-reasoning`: iterative search and verification | |
| - `table-centric`: table-first parsing | |
| - `form-centric`: forms and input structures prioritized | |
| Strategy selection can be: | |
| - Manual (user setting) | |
| - Automatic (router based on task signature) | |
| ## Self-Improving Agent Loop | |
| After each episode: | |
| 1. Compute reward breakdown | |
| 2. Extract failed and successful patterns | |
| 3. Update strategy performance table | |
| 4. Store high-confidence selectors in long-term memory | |
| 5. Penalize redundant navigation patterns | |
| ## Explainable AI Mode | |
| Each action can emit: | |
| - Why this action was chosen | |
| - Why alternatives were rejected | |
| - Which memory/tool evidence was used | |
| Example trace: | |
| ```text | |
| Action: EXTRACT_FIELD(price) | |
| Why: Pattern "span.product-price" had 0.93 historical confidence on similar domains. | |
| Alternatives rejected: ".price-box .value" (lower confidence 0.58), regex-only extraction (unstable on this layout). | |
| ``` | |
| ## Human-in-the-Loop | |
| Optional checkpoints: | |
| - Approve/reject planned action | |
| - Override selector/tool/model | |
| - Force verification before submit | |
| Intervention modes: | |
| - `off`: fully autonomous | |
| - `review`: pause on low-confidence steps | |
| - `strict`: require approval on all submit/fetch/verify actions | |
| ## Scenario Simulator Hooks | |
| Agents can be tested against: | |
| - Noisy HTML | |
| - Missing fields | |
| - Broken pagination | |
| - Adversarial layouts | |
| - Dynamic content with delayed rendering | |
| Simulation metrics: | |
| - Completion | |
| - Recovery score | |
| - Generalization score | |
| - Cost and latency | |
| ## APIs | |
| - `POST /api/agents/run` | |
| - `POST /api/agents/plan` | |
| - `POST /api/agents/override` | |
| - `GET /api/agents/state/{episode_id}` | |
| - `GET /api/agents/trace/{episode_id}` | |
| ## Dashboard Widgets | |
| - Live thought stream | |
| - Agent role timeline | |
| - Inter-agent message feed | |
| - Strategy performance chart | |
| - Confidence and override panel | |