Portia AI

Agents declare their intended actions before execution, using a structured planning phase that makes the action sequence visible and inspectable. This is implemented through an explicit planning step in the agent lifecycle where actions are enumerated and validated before any external side effects occur, enabling human review and interruption points.

Agents emit real-time progress updates during execution that can be consumed by a UI or monitoring system, with built-in hooks for human interruption that pause or cancel running actions. The framework streams execution state changes (action started, completed, failed) allowing external systems to monitor and intervene without polling.

Manages agent execution state through explicit context objects that are passed between planning and execution phases, maintaining separation between agent reasoning state, tool state, and human-provided overrides. State is structured as immutable or copy-on-write objects to prevent unintended mutations during concurrent or interrupted execution.

Provides a declarative schema system for defining available tools and actions that agents can invoke, with built-in validation of action parameters before execution. Schemas are used both for agent planning (to constrain what actions are available) and for runtime validation (to ensure parameters match expected types and constraints).

Provides a callback/hook system that fires at key points in the agent execution lifecycle (planning started, action selected, action executed, execution completed, interrupted). Hooks receive execution context and can be used to implement logging, monitoring, state persistence, or custom business logic without modifying agent code.

Enables agents to break down complex tasks into smaller, sequenced steps with explicit dependencies and ordering. Each step is planned and executed independently, with results from earlier steps available as context for later steps. This pattern supports both linear sequences and conditional branching based on step outcomes.

Provides mechanisms for humans to provide feedback, corrections, or new information during agent execution, which are incorporated back into the agent's context for subsequent planning and execution. Feedback can override agent decisions, provide missing information, or redirect the agent toward a different approach without requiring code changes.

Automatically captures detailed traces of agent execution including all planning decisions, action invocations, results, and state changes. Traces are structured for both human readability and machine analysis, enabling debugging, auditing, and replay of agent behavior. Traces include timestamps, parameters, results, and any errors or interruptions.

Supports coordination between multiple agents through explicit message passing and shared context. Agents can communicate results, request assistance, or coordinate on shared tasks. The framework manages message routing, context sharing, and execution ordering to prevent conflicts and ensure consistency.

Provides mechanisms to collect results from agent execution (including intermediate step results and final outputs) and format them for consumption by downstream systems. Supports multiple output formats (JSON, structured objects, natural language summaries) and can aggregate results from multi-step or multi-agent execution.