How It Works
When your agent stores messages in Acontext, a background monitor automatically:- Extracts tasks from the conversation — when an agent says “My plan is: 1. Search, 2. Create, 3. Deploy”, each step becomes a trackable task
- Detects success or failure — based on agent behavior, error signals, user confirmation, or task abandonment
- Records progress — what the agent actually did at each step, with specific details like file paths and API calls
- Captures user preferences — requirements and constraints the user mentioned during the conversation
Task Lifecycle
| Status | Meaning |
|---|---|
pending | Task identified but not started |
running | Agent is actively working on it |
success | Confirmed complete — agent moved on without errors, or user confirmed |
failed | Explicit errors, user abandonment, or user reported failure |
What Gets Extracted
Each task contains:- Description — what the user asked for, in their own words
- Status — current lifecycle state
- Progress — step-by-step record of what the agent did
- User preferences — requirements the user mentioned
Tasks represent user requests, not agent execution steps. “Book a restaurant in SF” is one task — the agent’s sub-steps (search, compare, reserve) are recorded as progress within that task.
Feeds Into Self-Learning
When a task reachessuccess or failed, it can feed into the self-learning pipeline. Acontext distills the outcome into structured analysis:
- Success → extracts the approach, key decisions, and generalizable patterns
- Failure → extracts the failure point, flawed reasoning, and prevention principles