Common context problems faced by data teams while building agents

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 09th, 2026 | 9 min read

Quick answer: What are the common context problems faced by data teams while building agents?

Data teams building AI agents rarely fail because the model cannot generate text. They fail because the agent does not have the right context about data, semantics, permissions, time, and organizational reality.

  • Missing or poor metadata: Agents can't tell which table, metric, or dashboard is the "right" one, so retrieval becomes a coin flip.
  • Broken permissions and safety context: Agents either surface sensitive data to the wrong people or fail silently because access rules are unclear.
  • Stale, time-blind context: Agents use outdated schemas, deprecated datasets, or old policies because they can't see what changed.

Below: why context is the bottleneck, permissions and safety failures.

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Why context is the real bottleneck for data teams building agents

Most enterprise agents start with model and prompt decisions. The real bottleneck appears later, when agents need to choose the right data, apply the right rules, and explain what they did. That is all context work.

1. Static prompts in a dynamic data environment


Many teams start by encoding rules and examples in long prompts. This works in demos, then collapses when schemas, policies, and owners change.

Prompts cannot keep up with evolving data products, metric definitions, or new domains. The result is brittle agents that need constant prompt surgery instead of drawing on a live context layer.

2. Fragmented context across tools and teams


The information an agent needs is spread across your data catalog, BI tool, wiki, ticketing system, and people’s heads.

When context is not aggregated and modeled, retrieval pipelines fall back to “whatever the vector store finds,” which amplifies documentation gaps rather than fixing them. In practice, retrieval quality and grounding dominate answer quality in most production RAG setups.

3. Hidden assumptions about data and semantics


Business logic lives in tribal knowledge: what “Active Customer” means, which “Revenue” metric is used in board decks, or when to exclude test data.

Agents without explicit ties to a business glossary or semantic layer cannot see these distinctions. They happily mix incompatible metrics or pick the wrong canonical dataset, which erodes trust even when outputs look fluent.

The most common context gaps in enterprise data

Even before LLMs, data teams struggled with metadata debt. Agents magnify this debt because they consume whatever context exists, good or bad.

1. Missing or low-quality metadata


Agents rely on titles, descriptions, tags, and classifications to decide what to read or retrieve. When most assets are unnamed, poorly described, or inconsistently tagged, retrieval behaves like blind search.

For agents, that translates into hallucinated joins, wrong tables, or irrelevant documents.

2. Ambiguous metrics and business definitions


If three dashboards define “Churn Rate” differently, humans might catch the discrepancy in a meeting. An agent will not.

Without a governed data glossary tied to physical assets, agents cannot distinguish between GAAP revenue, bookings, or ARR, or between test and production tables. This shows up as:

  • Conflicting answers to the same question
  • SQL that passes tests but uses the wrong metric
  • Reports that executives reject because “that’s not our number”

3. Lineage and provenance blind spots


Agents need to know not only what an asset is, but where it came from and how trustworthy it is. Without lineage and provenance:

  • They may pick a denormalized, downstream table instead of the certified source
  • They cannot explain how a number was produced
  • They cannot adjust for known data quality issues

This is where data lineage connected to tests, owners, and policies becomes part of the agent’s context, not just a diagram for humans.

Access, permissions, and safety context failures

Many of the hardest production incidents are not hallucinations. They are privacy or policy violations caused by missing or mis-modeled access context.

1. Agents retrieving data they should not see


If your retrieval layer has no notion of column sensitivity, row-level security, or regional residency, agents will happily surface PII, PHI, or restricted metrics to the wrong audience.

Permission checks need to happen during retrieval and tool execution, not after the model responds.

2. Over-restrictive policies that break retrieval


The opposite failure mode is equally common: policies are so coarse or opaque that agents cannot retrieve anything useful.

Symptoms include:

  • High rates of “I do not have access” responses
  • Agents defaulting to outdated public docs because fresh data is locked away
  • Teams bypassing the agent and rebuilding ad hoc extracts

3. Lack of auditable usage trails


Regulators and internal risk teams increasingly expect clear answers to “Which data did this agent see, and why?”

Without auditable logs tied to AI governance controls, teams cannot investigate incidents properly, prove compliance, or tune context safely.

Temporal and workload context: freshness, drift, and recency

Most agents today are time-blind. They treat a schema from last year and a hotfix from this morning as equally valid. For data teams, this is a shortcut to broken reports and mistrust.

1. Stale data and schema drift


Agents that generate SQL or call APIs often target datasets that no longer exist, have changed shape, or are no longer authoritative.

Common patterns include:

  • Queries against deprecated tables because “v1” and “v2” are indistinguishable
  • Misaligned joins after a schema change
  • Using historical snapshots as if they were live data

2. No sense of “what changed recently”


Context systems rarely expose change events: new owners, new quality rules, table deprecation, or major incidents. Yet humans rely heavily on “what changed” when debugging.

Agents need similar signals in their context graph:

  • Recent schema or contract changes
  • Incident tags on affected datasets
  • Freshness metrics and last-successful-load timestamps

3. Per-session and per-user context confusion


There is a difference between:

  • Stable organizational context (glossary, lineage, policies)
  • Long-lived memory about a user or team (persistent preferences)
  • Short-lived session context (current investigation, filters, dashboards)

Conflating these layers leads to bloated memory stores, privacy risk, and erratic behavior. Production memory systems typically separate these scopes and apply different retention rules.

Debugging agents without proper context instrumentation

When agents fail, most teams start with prompts instead of traces. Without structured observability, debugging becomes anecdotal and slow.

1. No traceability from answer back to sources


If you cannot move from a wrong answer to:

  • The exact documents, tables, or dashboards retrieved
  • The tool calls and intermediate steps taken
  • The policies and filters applied

Then you cannot systematically fix failure modes.

2. Lack of structured evaluation and error taxonomies


Many teams rely on spot checks or user complaints as their primary evaluation loop. That guarantees slow learning and biased feedback.

Instead, you need:

  • Clear error categories (wrong source, wrong time, permission error, misunderstanding, etc.)
  • Benchmarks built from real user questions and ground-truth answers
  • Regular replay and scoring, with emphasis on context failures

3. Limited observability into retrieval and tool calls


In multi-tool agents, the failure might come from a join across systems or a mis-ordered workflow step, not the LLM itself.

Logs must capture:

  • Which tools were invoked with which parameters
  • How retrieved items were filtered and ranked
  • Which context chunks were actually passed into the model

Agent debugging checklist for data teams

  • Capture full traces: retrieval sets, tool calls, prompts, and responses for real user sessions.
  • Label errors by type: wrong asset, wrong metric, permission issue, stale data, misunderstanding, or UI problem.
  • Tie traces back to assets: link trace IDs to datasets, dashboards, and glossary terms to see where failures cluster.

Connecting context engineering to governance and metadata

The biggest strategic mistake is treating agent context as a side project, separate from governance and metadata. For data teams, context engineering should extend existing controls, not reinvent them.

1. Treating context as a governed asset


Glossaries, lineage, quality rules, classifications, and policies already exist in many organizations. The problem is that they are not consistently modeled or exposed to agents.

Context engineering means:

  • Deciding which fields in your metadata management system should drive retrieval and ranking
  • Making business terms, certifications, and quality scores first-class filters in your agent stack
  • Defining ownership for context entities so someone is accountable when they drift

2. Using metadata platforms as context stores


A modern catalog or active metadata platform already knows:

  • Which datasets back which metrics and dashboards
  • Who owns which domain or product
  • Which assets are certified, deprecated, or sensitive
  • How data flows from source to BI or ML

Instead of building yet another “context DB,” use the catalog as your organizational memory, and create read-optimized views for retrieval and memory systems.

3. Operating context as a product with SLAs


If context is critical to AI behavior, it deserves product treatment:

  • Roadmap and scope: Start with one or two workflows, such as “explain this KPI” or “approve datasets for model training.”
  • SLAs and KPIs: Track coverage (owners, definitions, tests), usage (trusted assets selected), and outcomes (fewer incidents, faster resolutions).
  • Change management: Align context releases with governance councils, schema-change processes, and access reviews.

Conclusion

Context failures are systematic. They come from ambiguity, drift, fragmented metadata, and missing safety controls. Data teams can reduce these failures by treating context like governed infrastructure: define context contracts, anchor agents to glossary and lineage, make retrieval permission-aware, and invest in traceability plus evaluation. If you do that, agents stop guessing and start behaving like reliable automation in the data stack.

FAQs about common context problems faced by data teams while building agents

1. Why do most agent projects fail in enterprises?


Most agent projects fail because they lack reliable context about data, policies, and users, not because the model is too weak. When an agent cannot tell which dataset, metric, or document is authoritative, it guesses. Over time, that erodes trust and teams stop using it.

2. What types of context matter most for data agents?


Four types matter most: organizational (owners, glossary terms, policies), technical (schemas, lineage, tests), access and safety (permissions, sensitivity, residency), and temporal (freshness, recent changes, incidents). Strong agents have structured access to all four.

3. How can data teams start improving context without rebuilding everything?


Pick one workflow, such as explaining a KPI. Ensure the relevant assets have owners, definitions, lineage, and sensitivity labels. Then expose those signals to the agent through a consistent API or retrieval layer, and use early traces to find what context is missing.

4. What is the difference between a data catalog and an agent context store?


A data catalog is designed for humans to discover and govern assets. An agent context store is designed for machines to retrieve and reason over context reliably. Many teams use the same underlying metadata system for both, but they expose curated, machine-readable “context objects” for agents.

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