Agentic Reasoning

The ability to break a high-level, ambiguous objective into a structured, sequential plan of executable sub-tasks. Instead of requiring a human to specify every step, the agent analyzes the goal and generates its own directed acyclic graph (DAG) of actions.

• Key Mechanism: Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT) prompting
• Example: Given 'optimize today's production schedule,' the agent decomposes it into: (1) query current order book, (2) check machine availability, (3) analyze material inventory, (4) compute optimal sequence, (5) push to MES
• Contrast: A non-agentic model would only generate text about optimization, not a plan to execute it

The capacity to interact with external software, APIs, and databases by generating structured function calls. The model outputs a precise JSON schema that triggers a specific action in a connected system, bridging the gap between reasoning and real-world execution.

• Standard: Model Context Protocol (MCP) defines a universal interface for tool connection
• Example: The agent calls query_sensor(asset_id='CNC-42', metric='vibration') to retrieve live telemetry before deciding on a maintenance action
• Critical Requirement: Deterministic output formatting to ensure the generated JSON is syntactically valid and semantically correct for the target API

The agent's ability to evaluate its own intermediate outputs, detect errors or dead-ends, and iteratively revise its plan without human intervention. This creates a resilient execution loop that can recover from unexpected states.

• Core Loop: Act → Observe → Evaluate → Revise
• Example: If a tool call returns an error or an unexpected null value, the agent re-prompts itself with the error context and formulates an alternative approach
• Techniques: Reflexion, Self-Refine, and ReAct (Reason + Act) prompting patterns
• Outcome: Transforms brittle, linear scripts into robust, adaptive workflows

The engineering of short-term and long-term memory structures that allow the agent to maintain state across an extended, multi-turn task. Without memory, each reasoning step would operate in isolation, losing track of prior actions and retrieved data.

• Short-Term: The active context window holding the immediate conversation, tool outputs, and intermediate reasoning traces
• Long-Term: External stores like vector databases or knowledge graphs that persist critical information beyond a single session
• Example: After querying a maintenance log in step 2, the agent stores the retrieved failure history in its working memory so it can reference it during the final diagnostic step without re-querying

The agent maintains an internal representation of the task state and dynamically adjusts its plan as new information is acquired. This is distinct from simple linear scripting; the agent understands dependencies between steps and can re-plan when preconditions change.

• Representation: A task graph where nodes are actions and edges are dependencies
• Dynamic Re-planning: If a required machine is reported as down during execution, the agent autonomously re-routes the production sequence to an available asset
• Key Distinction: This is closed-loop control at the cognitive level, not just executing a static, pre-defined workflow

Every action and conclusion is anchored in retrieved, verifiable data rather than the model's parametric knowledge alone. This prevents hallucination in high-stakes industrial contexts where an incorrect command could halt production or damage equipment.

• Mechanism: Retrieval-Augmented Generation (RAG) fetches authoritative data from manuals, sensor feeds, and databases before the agent formulates a response or action
• Example: Before recommending a temperature adjustment, the agent retrieves the specific operating envelope for that material from the manufacturing knowledge graph
• Result: Decisions are auditable and traceable back to a specific, cited data source