A context engineer is building a Databricks research agent that produces analyst reports where every claim must cite a verifiable source: a reviewer must be able to trace each citation back to its exact original source text. At each step, the agent calls a retrieval tool that returns several short source snippets, writes a reasoning trace explaining how those snippets support its conclusions, and appends both the snippets and the reasoning to a running context reused by later steps. As the run grows, the accumulated context pushes per-step token cost above the team's budget, so the context must be compacted to reduce token spend.
Which compaction strategy meets both the citation requirement and the need to reduce tokens?

• A. Recency eviction: drop the earliest steps' snippets and reasoning as new steps arrive, keeping only the most recent context that fits the token budget.
• B. Aggressive compaction: replace each step's snippets and reasoning with a brief model-written summary before passing context to later steps, since summaries reclaim the most tokens.
• C. Snippet summarization: replace each retrieved snippet with a short paraphrase of what it says to save tokens, while keeping every reasoning trace in full.
• D. Conservative compaction: keep the retrieved snippets verbatim but compress the reasoning traces and drop redundant text, accepting a smaller token saving in order to preserve the source text citations depend on.

A context engineer is building a customer support agent on Databricks. The agent's session-initialization routine must populate a context object before turn 1 begins; the context object is then passed to every turn handler. The agent needs the user's plan tier, region, and a few entitlement flags from the Lakebase customer_profile table in that context object. The values must be present before the first turn handler executes and may be referenced on any subsequent turn. The values do not change during a session, but could change across different sessions. The team wants to minimize per-turn Lakebase round-trips and the latency they add to turn responses.
Which retrieval approach should the engineer implement for this agent architecture?

• A. Query Lakebase for the plan tier, region, and entitlement flags during each turn that references them, so the agent pulls the current values from the table on those turns.
• B. Pre-load the user's plan tier, region, and entitlement flags from Lakebase at session start, and re-fetch them on a fixed five-minute cadence to keep the agent's copy in sync with Lakebase.
• C. Query Lakebase for the plan tier, region, and entitlement flags once and store them in a shared cross- session cache, then have each session read its values from that cache at session start.
• D. Pre-load the user's plan tier, region, and entitlement flags from Lakebase into the agent's per-session context object at session start, and reuse the loaded values for the rest of the session.

A context engineer is investigating an AI agent deployed on Databricks that handles customer support operations. The agent has two MCP tools registered: one described as "Searches customer support tickets and open case records for a given customer", and another described as "Looks up customer issue history and unresolved case details by customer ID".
When users ask whether a customer has any open issues, the agent randomly chooses between the two tools across repeated identical requests. When users ask about a customer's past resolved issues, the agent similarly misfires between the two tools. The engineer has reviewed the tool call logs and confirmed that the agent cannot reliably distinguish which tool to invoke.
Which overlap between the two tool descriptions is causing the agent to select tools ambiguously?

• A. Both descriptions claim the same functional scope: each covers open cases (open case records and unresolved case details) and historical records (support tickets and issue history), leaving no functional differentiator.
• B. Both descriptions omit the data source or system name, so the agent cannot determine which backend database each tool queries when forming a retrieval plan.
• C. Both descriptions reference a customer identifier as the lookup parameter, making the input signature indistinguishable to the agent when resolving tool selection.
• D. Both descriptions use action verbs (Searches and Looks up) that belong to the same semantic category, causing the agent to treat the tools as interchangeable retrieval operations.

A context engineer is developing a multi-agent research pipeline on Databricks. A coordinator agent divides an incoming scientific literature query into two parallel subtasks: a citation-retrieval agent and a methodology- analysis agent. Both agents independently receive only the original user query, produce their outputs, and return them to the coordinator, which then invokes a synthesis agent. The synthesis agent produces a final report that contradicts the citation-retrieval findings because it relies solely on the methodology-analysis output and has no visibility into retrieved citations.
Which two context propagation changes would prevent the conflicting outputs in the synthesis agent?
Choose two

• A. Update the synthesis agent's input schema to accept a structured context object containing both citation- retrieval results and methodology-analysis results as named fields.
• B. Replace the parallel dispatch pattern with a sequential chain so the methodology-analysis agent runs first, and the citation-retrieval agent runs afterward, without changing what context is forwarded to the synthesis agent.
• C. Configure the coordinator to forward only a high-level methodology summary into the synthesis agent's context while omitting the underlying citation-retrieval data, to keep the context window small.
• D. Configure the coordinator agent to merge outputs from the citation-retrieval and methodology-analysis agents into a single context payload before invoking the synthesis agent.
• E. Broadcast every agent's full raw interaction history, including intermediate prompts and tool calls, directly to the synthesis agent, allowing the LLM to self-structure citation results from methodology analysis.