Automation has long been part of our technological journey, but the shift toward building truly agentic systems—ones that combine autonomy, collaboration, and self-improvement—demands a precise and cohesive framework. In this post, I present a formal model for Automated Agentic Task-Handling that unifies system architecture, lifecycle logic, and governance into a coherent whole. This model provides a rigorous basis for defining how agents perceive their environment, make decisions, and act upon tasks with minimal human intervention.
By examining the core capabilities of intelligent agents, exploring task lifecycles and state machines, and detailing tool ecosystems and control mechanisms, I illustrate how each component operates within a well-structured framework. This formal model aims to ensure reliability, adaptability, and interoperability in multi-agent ecosystems—offering a blueprint that can scale from simple, single-role agents to advanced networks that coordinate complex workflows. Whether you’re researching multi-agent collaboration or designing systems that harness autonomous capabilities at scale, you’ll find practical foundations and guiding principles in this comprehensive model.
Agent Architecture
An agent is an autonomous entity characterized by its task performance, tool utilization, and interaction capability: respectively, the ability to achieve objectives, the capacity to utilize instruments for task execution, and the competence to interact with its environment, including other agents.
Core Capabilities
Agents can use a combination of three core capabilities to achieve objectives. These capabilities are defined as follows:
- Perception Module: Input processing system which analyzes structured and unstructured data.
- Decision Engine: State transition controller implementing formal transition rules.
- Action Interface: Standardized interaction protocol for tool activation
Logic and Structure
Intelligent agents need flexible, dynamic workflows to handle diverse tasks effectively. This section outlines the core structural principles of an agent, which is designed to balance dynamism with predictable and reliable behaviour.
- Objective-Oriented Behaviour: Agents are designed to achieve specific goals which are defined through formal specifications.
- Decision Making: Agents make decisions based on the task context, knowledge, tool descriptions, tool schemas, and communication with other agents or humans. These decisions are based on quantifiable data rather than subjective interpretations.
- Tool Utilization: Agents are defined with a set of tools at their disposal. The agent can select and utilize any number of appropriate tools to perform task-related actions.
- Communication and Collaboration: Agents can communicate with each other to delegate tasks, request information or context, and coordinate nested task execution. Agent communication and collaboration operates on standardized protocols.
- Adaptation and Optimization: Agents can learn from experience, improve their performance, and adapt to changing environments via context switching. This is achieved by parametric adjustments for tool selection strategies and updating the knowledge base (explained in more detail in the Agent Governance section).
Agent Properties
| Property Name | Description | Data Type |
|---|---|---|
| ID | A unique identifier for the agent | UUID |
| Capabilities | List of tools the agent can utilize | Array<Tool IDs> |
| Status | Current operational state | Enum(Idle, Working, Paused, Error, Recovering, Initializing) |
| Current Task ID | ID of the task the agent is actively working on | UUID (optional if idle) |
| Communication Channels | Methods for the agent to communicate with other agents or external systems | Array<Enum(API, Message Queue, Shared Memory)> |
| Configuration | Agent-specific configuration settings | Object { modelParams, etc… } |
| Metrics | Key performance indicators for the agent, used for control mechanisms. | Object { tasksCompleted, errorsEncountered, averageTaskCompletionTime, messagesSent, messagesReceived } |
| Knowledge | Persistent state container for operational history | Vector Database |
| Tool Requirements | Configurable requirements to optimize tool selection and escalation | Object { maxAcceptableLatency, minSuccessRate, costSensitivity: Enum(low, medium, high), costThreshold } |
| Weights | Configurable weights which the Overseer Agent periodically adjusts to optimize performance based on governance policies | Object { latency, successRate, costEfficiency } (optional) |
| Models | The default model for the agent with an optional list of alternative models in case the default model fails. | UUID | Array<{ default: UUID; alternatives?: Array<UUID> }> |
Agent Typology
Generic agents
Common agent roles within an agentic system:
- Overseer Agent
- Function: Monitors the overall system, manages tasks, controls other agents, handles escalations, optimizes agent tool selection, and performs system-level functions such as resource allocation.
- Capabilities: Task management, error handling, agent control, monitoring, reporting, and potentially higher-level reasoning and planning.
- Logic: Mainly focuses on system-wide efficiency, prioritizing tasks, allocating resources, and ensuring operations run smoothly.
- User Interaction Agent
- Function: Handles communication with users, organizes input, provides information, and reports task results.
- Capabilities: Natural language processing, multi-modal, user interface interaction, validating input, and formatting output.
- Logic: Focuses mainly on user experience, communicating clearly, and efficiently exchanging information between the user and the agentic system.
- Summarizer Agent
- Function: Condense large amounts of data and information into concise summaries.
- Capabilities: Text summarization (extractive, abstractive), information extraction, topic classification, and natural language generation.
- Logic: Focuses on information relevance, conciseness, and maintaining key information by reducing redundancy
Application Agents
Agent types categorized by their primary application or workflow:
- Chat Agent: Specializes in conversational interactions, answering question, providing support, and engaging in dialogue (e.g. customer service, personal assistant).
- Workflow Agent: Executes predefined workflows and automates multi-step processes (e.g. data processing, order fulfillment).
- Data Analysis Agent: Focuses mainly on analyzing data, extracting key insights, generating reports, and performing any data-driven tasks (e.g. market research agent, analyzing customer behavior)
- Content Creation Agent: Generates text, audio, images, videos, or other content based on prompts or instructions (e.g. social media post generator, article writing agent)
- Information Retrieval Agent: Specialized in finding and retrieving information from data sources, useful for grounding (e.g. web search agent, knowledge base query agent).
- Planning and Scheduling Agent: Creates plans for workflows, schedules tasks, and optimizes allocation of resources (e.g. project management, schedule meetings).
Tasks and Their Lifecycle
A task represents a unit of work with defined objectives, input parameters, and success criteria. It is the central entity driving agentic behaviour.
Task Stages (State Machine)
Tasks can progress through defined stages which are managed by a state machine. Its lifecycle operates through nine primary states:
- Pending: Task is created but not started yet
- Initializing: Agent is preparing to start the task (e.g. gathering resources, tool selection)
- In Progress: Agent is actively working on the task
- Awaiting Input: Task is paused, awaiting external input (e.g. user input, data from another task)
- Review Required: Task is completed or reached a milestone which requires review (e.g. by an overseer agent or user)
- Paused: Task is temporarily paused and can be resumed
- Cancelled: Task is terminated and can’t be resumed
- Completed: Task is successfully finished
- Failed: Task execution encountered an unrecoverable error
Transition Rules
A task transitions between states by following guarded rules. These transitions can be triggered by:
- System Logic: Control mechanisms and overseer agents can force a state transition (e.g. escalating stalled tasks)
- External Events: User input, scheduled time-based triggers, and signals from other tasks or agents
- Agent Actions: An agent successfully completes a stage, decides to pause, or encounters an error
Example Transition Rules
| Current State | Next State | Trigger |
|---|---|---|
| Pending | Initializing | Task is assigned to an agent |
| Initializing | In Progress | An agent begins executing the task logic |
| In Progress | Awaiting Input | The agent requires external data or user input to continue |
| In Progress | Completed | The agent has determined the task objective is met |
| In Progress | Failed | The agent encounters an unrecoverable error while executing the task logic |
| In Progress | Paused | An agent or control mechanism calls the pause signal |
| Paused | In Progress | An agent or control mechanism calls the resume signal |
| Any State (except completed) | Cancelled | A user or control mechanism calls the cancel signal |
| Completed | Review Required | Task type requires review from a user or agent upon completion |
| Review Required | In Progress | The reviewer requested a rework, with optional or required feedback based on the task type |
| Review Required | Completed | The review is approved by the reviewer |
A complex task may implement nested state machines, in which a parent task manages and coordinates child tasks through supervisor states like Monitoring and Reconciliation.
Task Properties
| Property Name | Description | Data Type |
|---|---|---|
| ID | A unique identifier for the task | UUID |
| Objective | Structured or unstructured data representing the objective of the task | String | Object |
| Status | Current state of the task | Enum(Pending, Initializing, InProgress, AwaitingInput, ReviewRequired, Completed, Failed, Paused, Cancelled) |
| Priority | Dynamic priority level of the task. Determined and used by the Overseer Agent for task routing. | Tuple: (base: ℕ, adjustments: Δᵢ ∈ ℝ) |
| Context | Relevant information and data associated with the task | Typed context tuple: (τ: type, δ: data, ν: version) |
| Output Schema | Expected result structure specified by the user or other agents | Schema-Enforced Object (Zod, JSON) |
| Deadline | Optional time limit for task completion | Timestamp |
| Created at | Time when the task was initiated | Timestamp |
| Last Modified | Time when the task was last updated | Timestamp |
| Primary Agent | ID of the agent currently in charge of the task | UUID |
| Responsible Agents | IDs of all agents assigned to the task | Array<UUID> |
| Parent Task ID | If the task is a subtask, this points to the parent task | UUID |
| Output | Output of the task upon completion, conforms to the Output Schema | Inferred from Output Schema |
| Error Log | Record of any errors or issues encountered during task execution | Array<Error | String> |
| Comments | A list of comments by agents or users. | Array<{ createdAt: Timestamp, text: String }> |
| Checklist | An optional list of required validation predicates | Array<String> |
| Type | The category type for the task, used to determine how the agent should execute the task | Enum(Basic, Decision, Loop) |
Tool Ecosystem
A tool is an external resource or capability that agents can utilize to gather information and perform actions.
Tool Properties
| Property Name | Description | Data Type |
|---|---|---|
| ID | Unique identifier for the tool | UUID |
| Description | Functional specification: F(input: Σ, output: Δ) | String (Optional) |
| Parameters | Structure of the parameters required to call the tool | Schema-Enforced Object (Zod, JSON) |
| Execute | Activation protocol: Π(input) → output | Function (optional) |
| Version | An optional version number to ensure compatibility and accurately tracking metrics. | Number (optional) |
| Metrics | Key metrics used for optimizing agents by the Overseer Agent | Object { successRate, avgLatency, cost } |
Logic and Structure
The tool ecosystem is designed to empower agents through modularity, abstraction, and interoperability. Tools act as self-contained units of functionality, easily integrated and managed within the agentic system.
- Encapsulated Functionality: Tools provide specific, well-defined functionalities that agents can invoke.
- Abstraction: Tools abstract away the underlying implementation details, allowing agents to focus on task logic.
- Interoperable: Tools are designed to be easily integrated into the agentic system and can be used by multiple agents.
- Modular: Tools can be added, removed, or updated independently without affecting the core agent system architecture.
Measuring Tool Effectiveness
Having a systematic evaluation of tool performance is necessary to ensure optimal resource allocation and continuous improvement. For this, we use a compatibility matrix formula which enables:
- Objective comparison between alternative tools
- Dynamic tool selection based on current system priorities
- Performance benchmarking against SLAs
- Resource optimization using weight adjustments
Compatibility Score
Where:
This compatibility matrix formula is used by an Overseer Agent for optimizing tool selection and agent weights based on system events (e.g. peak hours, cost saving mode, etc…) and governance policies.
Operational Dynamics
Agent delegation occurs through three primary patterns:
- Synchronous delegation
- Primary agent pauses until sub-task completion
- Used for sequential dependencies
- Example: Onboarding agent that requires security clearance before giving system access
- Parallel Delegation
- Parallel sub-task execution with synchronization points
- Enables workload distribution as commonly shown in research delegation patterns
- Implements eventual consistency checks via reconciliation states
- Full Redirect
- Complete responsibility transfer
- Utilizes a state machine handoff protocol
- Transfer task state & context, current progress (if any), original agent ID, delegation reason, and communication channels
- Use of atomic transactions, acknowledgments, state transition locking, and idempotency to ensure consistency
- Commonly used in tiered customer support escalation systems
Control Mechanisms
This model employs three mechanisms for controlling agents, with strict governance policies to ensure continuous stability.
Agent Control
- Control Interface: A system component responsible for managing the entire agent lifecycle.
- Start: Activates an agent, making it available to take on tasks.
- Stop: Deactivates an agent, preventing it from taking on new tasks and optionally pausing any active tasks for which the agent is in charge.
- Pause: Temporarily suspend an agent’s activity, allowing it to be resumed later.
- Resume: Restarts a paused agent, allowing it to continue where it left off, with optional additional context, comments, etc…
- Triggers: Control actions can be triggered by:
- User commands
- System events
- Load balancing requirements, error conditions, and scheduled maintenance periods
- Overseer agents
Agent Review
- Purpose: Evaluate agent performance, identify areas for improvement, and ensure agents are functioning as intended based on objective performance metrics and optionally defined weights.
- Review Metrics: Data collected about agent performance (see Agent Properties section)
- Review Process:
- Automated Review: The system automatically analyzes agent metrics and flags any performance issues or anomalies.
- Human Review: Human overseers examine agent logs and metrics to asses performance and provide feedback.
- Outcomes:
- Performance Reports: Summaries of agent performance across various time periods
- Adjustments: Changes to the agent or model configuration to improve performance
- Retraining: Updating agent models, requirements, and context based on review findings
- Escalation: If an agent continuously fails or encounters unrecoverable errors, the issue(s) may be escalated for human review
Agent Escalation
- Purpose: Handle any situation where an agent is unable to complete the task objective or encounters a problem which is unsolvable with its capabilities.
- Triggers:
- Task failure (agent repeatedly fails to complete a task)
- Error threshold is reached
- Timeout (task exceeded the deadline)
- Complexity: The task is too complex for the agent to solve with its designed capabilities (tools, knowledge, etc…)
- Escalation Levels:
- Delegate to Overseer Agent: The task must be redirected to a more capable agent by the Overseer Agent
- Human Intervention: The task must be escalated to a human for additional context or guidance
- System Alert: Unrecoverable errors or failures that require attention from the agentic system’s administrator. Alerts can be sent via configurable built-in tool calls (Email, Slack, etc…) or by passing a custom tool to the agent.
- Logic: Predefined rules and the dynamic decision engine determine the escalation path based on the trigger and context of the task. Predefined rules are defined in both the agent’s Configuration and the Overseer Agent’s logic.
Agent Governance
The orchestration of agents in this model employs a three-layer governance system:
- State Observability
- Continuous monitoring for several attributes:
- State duration percentages
- Frequency of transitions
- Distribution of exception types
- Continuous monitoring for several attributes:
- Feedback Integration
- Immediate: Task success/failure signals
- Delayed:
- State sequence reviews
- Delegation effectiveness
- Cost-efficiency and other key metrics
- Escalation Protocols
- Vertical: Transfers upwards to superior agents (e.g. Overseer Agent)
- Horizontal: Peer review / consultation with other agents
Escalation protocols are implemented through Escalation Review States which are responsible for creating an analysis of contributing factors and resolution path recommendations.