Context Engine

A context engine is a service that dynamically assembles the most relevant structured memory for a given task, query, or agent state. It combines semantic retrieval, temporal awareness, entity relationships, and importance scoring to select what context matters now—not just what's similar, but what's relevant, recent, and actionable.

Unlike static retrieval or simple similarity search, a context engine reasons over multiple dimensions: recency (what changed recently?), relationships (who owns this?), task alignment (what's needed for this query?), and importance (what's critical vs. noise?). This intelligent assembly powers agent memory, operating reviews, and real-time decision support.

The outcome is agents that receive precisely the right context at the right time, without noise or outdated information.

Why it matters

• Prevents context overload: Selects the most relevant memory instead of dumping all available data—agents reason better with focused context.
• Combines multiple signals: Integrates semantic similarity, temporal recency, entity relationships, and importance—not just one dimension.
• Enables time-aware retrieval: Surfaces recent changes and current state, not outdated facts—critical for accuracy and decision-making.
• Supports task-specific context: Different tasks need different memory—a status brief needs recent changes; debugging needs error history.
• Reduces hallucinations: Grounding agents in relevant, verified memory prevents inventing plausible-but-wrong answers.
• Improves agent performance: Focused, high-quality context leads to better reasoning, faster decisions, and more reliable outputs.

How it works

A context engine operates through multi-dimensional retrieval and assembly:

• Query Understanding → The engine analyzes the task or query: What's being asked? What entities are involved? What time frame matters?
• Semantic Retrieval → Vector similarity search identifies content related to the query's meaning.
• Temporal Filtering → Recent events and state changes are weighted higher—"show me what changed for Project X this week."
• Relationship Traversal → The knowledge graph is queried: "What tasks are owned by Alice and block Project X?"
• Importance Scoring → Memory is ranked by relevance, recency, and criticality—high-priority items surface first.
• Context Assembly → Selected memory is composed into a structured context package: facts, relationships, timeline events, and current state.
• Delivery → The assembled context is provided to the agent, LLM, or application—optimized for the context window and task needs.

This pipeline ensures agents receive the right memory at the right time.

Comparison & confusion to avoid

Examples & uses

Status brief context assembly
Task: "Generate a weekly brief for Project Alpha." The context engine queries: (1) Semantic: documents mentioning Alpha. (2) Temporal: state changes in the last week. (3) Relationship: tasks owned by Alpha team members. (4) Importance: blockers and decisions. Assembled context: "3 tasks completed, 2 new blockers, 1 ownership transfer, 1 critical decision needed."

Debugging context for incident response
Task: "Why is Service X failing?" The context engine retrieves: (1) Recent error logs (temporal). (2) Recent deployments (events). (3) Services that depend on X (relationships). (4) Prior incidents with similar symptoms (semantic similarity). Assembled context enables rapid diagnosis.

Personalized agent assistance
Task: "Help Alice with her tasks." The context engine queries: (1) Tasks owned by Alice (relationship). (2) Recent updates or blockers (temporal). (3) Alice's communication preferences (semantic facts). Assembled context: "Alice owns 3 tasks, 1 is blocked, she prefers async updates via Slack."

Best practices

• Weight recency heavily: Recent events are more relevant than old ones—implement time decay in scoring.
• Combine semantic + structural + temporal: Don't rely on one signal—hybrid retrieval improves precision.
• Tune for task types: Status briefs need recent changes; historical analysis needs broader timelines—context engine should adapt.
• Limit context to window size: Assemble the best memory that fits the LLM's context window—quality over quantity.
• Surface provenance: Include where memory came from (document, timestamp, author)—enables users to verify and trust context.
• Monitor retrieval quality: Track precision and recall—are agents getting the right context? Adjust scoring weights accordingly.

Common pitfalls

• Treating all memory as equally relevant: Not all facts matter for every task—context engines must prioritize and filter.
• Ignoring temporal signals: Retrieving semantically similar but outdated memory leads to wrong conclusions—time awareness is critical.
• Over-relying on vector similarity: Embeddings alone miss relationships and importance—hybrid approaches perform better.
• No task adaptation: A one-size-fits-all context assembly degrades performance—tailor retrieval to the task.
• Overfilling the context window: Cramming in all available memory is wasteful—select the most relevant subset.

See also

• Agent Memory — Persistent context powered by context engines
• Semantic Retrieval — Meaning-based context fetching
• Temporal Memory — Time-aware memory for recency filtering
• Knowledge Graph — Relationship structure for traversal queries
• Memory Index — Lookup layer for fast retrieval

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See how Graphlit implements Context Engines for agent memory → Agent Memory Platform