Why Agent Projects Fail (and How Data Structure Fixes It)

Most AI agent projects fail not because of the models, but because agents can't reliably access the right data at the right time. We break down the common failure patterns and how structured data views solve them.

The Promise vs. The Reality

We've all seen the demos: an AI agent that answers questions about your business, pulls customer data on demand, and makes decisions based on real-time information. It looks magical. So you build one.

Then reality hits.

The agent can't find the data it needs. It queries the wrong tables. It returns incomplete results. It times out on complex queries. Users get frustrated. The project gets shelved.

I've watched this happen dozens of times. Teams spend weeks or months building sophisticated agent workflows, only to discover that the hardest part isn't the AI—it's the data access layer.

Why Agents Fail: The Data Access Problem

Most agent failures trace back to one root cause: agents can't reliably access the right data at the right time.

The "Data Not Found" Problem

Agents need to know where data lives. In a typical organization, customer data might be in:

• HubSpot (CRM)
• Snowflake (data warehouse)
• Postgres (application database)
• Zendesk (support tickets)
• Stripe (payments)

An agent asking "what's the status of customer X?" needs to query multiple systems. Without a unified view, the agent either:

• Queries one system and misses critical context
• Queries all systems and gets inconsistent results
• Gives up and returns "I don't have that information"

The "Wrong Data" Problem

Even when agents find data, they often get the wrong data. Here's what we see:

Schema Confusion: An agent queries customers.email when it should query users.email_address. Different systems use different naming conventions, and agents don't know the difference.

Relationship Errors: An agent joins orders to customers on the wrong key, returning orders for the wrong customer. Or it doesn't join at all, missing critical relationships.

Stale Data: An agent queries a cached view that hasn't been updated in days, giving users outdated information.

Incomplete Data: An agent queries customers but misses the customer_preferences table, so it can't answer questions about customer preferences.

The "Too Much Data" Problem

Sometimes agents get too much data:

Unbounded Queries: An agent queries "all customers" and gets 2 million rows. The query times out, or the response is too large to process.

Over-Joining: An agent joins 5 tables when it only needs 2, creating a massive result set that's slow to process and expensive to run.

No Filtering: An agent queries all historical data when it only needs the last 30 days, wasting compute and time.

The "Permission Denied" Problem

Agents hit permission walls:

Database Access: The agent's database user doesn't have permission to query certain tables, so it fails silently or returns partial results.

API Rate Limits: The agent hits API rate limits when querying external services, causing intermittent failures.

Cross-System Access: The agent can query Snowflake but not HubSpot, so it can't answer questions that require both.

The Real Failure Patterns We See

Let me walk you through the actual failure patterns we've observed in production.

Pattern 1: The Fragmented Data Problem

What Happens: An agent needs customer information, but customer data is spread across 5 different systems. The agent queries one system, gets partial information, and makes decisions based on incomplete data.

Example: A support agent is asked "What's the status of customer X's recent order?" The agent queries the support system (Zendesk) and finds a ticket, but it doesn't query the order system (Snowflake) to get order status. It tells the user "I found a support ticket" but can't answer the actual question.

Why It Fails: The agent doesn't have a unified view of customer data. It can't easily join data across systems.

The Fix: Create a unified customer view in Pylar that joins data from HubSpot, Snowflake, Zendesk, and Stripe. Learn how to create data views and join data across systems. The agent queries one view and gets complete customer context.

-- Unified Customer View in Pylar
SELECT 
  h.customer_id,
  h.customer_name,
  h.email,
  h.company_name,
  s.order_count,
  s.total_revenue,
  s.last_order_date,
  z.open_tickets,
  z.last_ticket_date,
  st.subscription_status,
  st.plan_name
FROM hubspot.customers h
LEFT JOIN snowflake.order_summary s 
  ON h.email = s.customer_email
LEFT JOIN zendesk.ticket_summary z 
  ON h.email = z.customer_email
LEFT JOIN stripe.subscriptions st 
  ON h.email = st.customer_email
WHERE h.is_active = true;

Now the agent can answer "What's the status of customer X's recent order?" by querying a single view that has all the context.

Pattern 2: The Schema Mismatch Problem

What Happens: Different systems use different schemas. The customers table in Postgres has email, but the users table in Snowflake has email_address. The agent queries the wrong field and gets no results.

Example: An agent is asked to "find all customers in California." It queries customers.state = 'CA' in Postgres, but the actual field is customers.region and the value is 'California', not 'CA'. The query returns zero results.

Why It Fails: Agents don't understand your specific schema. They make assumptions based on common patterns, but your schema might be different.

The Fix: Create views in Pylar that normalize schemas. Map email_address to email, region to state, and standardize values. The agent queries a consistent schema.

-- Normalized Customer View
SELECT 
  customer_id,
  email,  -- Normalized from email_address, user_email, etc.
  name,
  state,  -- Normalized from region, location, etc.
  signup_date
FROM customers_normalized_view
WHERE state = 'California';

Pattern 3: The Performance Timeout Problem

What Happens: An agent writes a query that works in development but times out in production. The agent retries, creating a cascade of slow queries that bring down the database.

Example: An agent is asked to "analyze all customer orders from the last year." It writes a query that scans the entire orders table (2 million rows) and joins to customers and products. The query takes 45 seconds and times out. The agent retries 3 times, creating 3 slow queries that lock tables and slow down other applications.

Why It Fails: Agents don't think about query optimization. They write queries that work but aren't efficient.

The Fix: Create optimized views in Pylar with proper indexes, filters, and aggregations. The agent queries pre-computed, optimized views instead of raw tables.

-- Optimized Customer Analytics View
CREATE VIEW customer_analytics_optimized AS
SELECT 
  customer_id,
  customer_name,
  COUNT(order_id) as order_count,
  SUM(order_total) as total_revenue,
  AVG(order_total) as avg_order_value,
  MAX(order_date) as last_order_date
FROM orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 YEAR)
  AND order_status != 'cancelled'
GROUP BY customer_id, customer_name
HAVING order_count > 0;

This view is pre-aggregated and filtered, so the agent's query is fast.

Pattern 4: The Permission Denied Problem

What Happens: An agent tries to query a table it doesn't have permission to access. The query fails, and the agent can't answer the question.

Example: An agent is asked to "show me all customers with high-value orders." It tries to query the orders table, but the agent's database user only has permission to query customer_summary_view. The query fails with "permission denied."

Why It Fails: Database permissions are too coarse. You can't easily give agents access to specific columns or filtered rows.

The Fix: Create views in Pylar that define exactly what data agents can access. Agents query through views, not raw tables, so you have fine-grained control.

-- High-Value Customers View (Agent-Accessible)
CREATE VIEW high_value_customers_view AS
SELECT 
  customer_id,
  customer_name,
  email,
  total_revenue,
  order_count,
  avg_order_value
FROM customer_summary
WHERE total_revenue > 10000
  AND order_count >= 5;

The agent can query this view, but it can't access the underlying orders table or see customers below the threshold.

Pattern 5: The Stale Data Problem

What Happens: An agent queries a cached view or a system that hasn't synced recently, returning outdated information.

Example: An agent is asked "what's the current subscription status for customer X?" It queries a cached view that was last updated 3 days ago. The customer canceled their subscription yesterday, but the agent reports it as "active."

Why It Fails: Data freshness isn't guaranteed. Cached views, ETL pipelines, and API syncs all introduce delays.

The Fix: Use Pylar's real-time querying for critical data. For less critical data, use views with clear freshness indicators or TTLs.

-- Real-Time Customer Status View
SELECT 
  customer_id,
  subscription_status,  -- Queried directly from Stripe API
  last_updated,  -- Timestamp of when data was fetched
  CASE 
    WHEN last_updated < DATE_SUB(NOW(), INTERVAL 1 HOUR) 
    THEN 'stale' 
    ELSE 'fresh' 
  END as data_freshness
FROM stripe_subscriptions_realtime;

Pattern 6: The Context Loss Problem

What Happens: An agent makes multiple queries but loses context between them. It can't connect related information.

Example: An agent is asked "why did customer X churn?" It queries customer data and finds the customer canceled. It queries support tickets and finds 3 open tickets. But it doesn't connect the two—it can't answer "the customer churned because of unresolved support issues."

Why It Fails: Agents make discrete queries. They don't maintain context or understand relationships between queries.

The Fix: Create views that pre-join related data, giving agents complete context in a single query.

-- Customer Churn Analysis View
SELECT 
  c.customer_id,
  c.customer_name,
  c.subscription_status,
  c.churn_date,
  c.churn_reason,
  COUNT(DISTINCT t.ticket_id) as open_tickets_at_churn,
  MAX(t.created_date) as last_ticket_date,
  AVG(t.satisfaction_score) as avg_satisfaction_score
FROM customers c
LEFT JOIN support_tickets t 
  ON c.customer_id = t.customer_id 
  AND t.status = 'open' 
  AND t.created_date <= c.churn_date
WHERE c.subscription_status = 'canceled'
GROUP BY c.customer_id, c.customer_name, c.subscription_status, c.churn_date, c.churn_reason;

Now the agent can answer "why did customer X churn?" with complete context in one query.

How Structured Data Views Solve These Problems

Structured data views—governed SQL views that define exactly what data agents can access—solve all of these failure patterns.

What Are Structured Data Views?

A structured data view is a SQL query that:

• Defines exactly what data agents can access
• Joins data across multiple systems
• Normalizes schemas and values
• Filters and aggregates data for performance
• Controls access at the view level

Agents query through views, not raw tables. This gives you:

• Unified Access: One view can join data from multiple systems
• Schema Normalization: Views map different schemas to consistent formats
• Performance Optimization: Views can be pre-aggregated and indexed
• Access Control: Views define exactly what data is accessible
• Freshness Control: Views can query real-time or cached data as needed
• Context Preservation: Views pre-join related data for complete context

The Pylar Approach

Pylar is built around structured data views. Here's how it solves each failure pattern:

Fragmented Data: Create views that join data across HubSpot, Snowflake, Zendesk, Stripe, and more. Agents query one view and get complete context.

Schema Mismatch: Views normalize schemas. Map email_address to email, standardize values, and create consistent interfaces.

Performance Timeouts: Views are optimized with indexes, filters, and aggregations. Agents query fast, pre-computed views instead of slow, raw tables.

Permission Denied: Views are the only access level. Agents can only query through views, so you have complete control over what data is accessible.

Stale Data: Views can query real-time data or cached data with freshness indicators. You choose the right approach for each use case.

Context Loss: Views pre-join related data. Agents get complete context in a single query, so they don't lose information between queries.

Building Views That Work

Here's how to build views that prevent agent failures:

Start with the Agent's Needs

Before creating a view, understand what the agent needs to do:

• What questions will it answer?
• What data does it need?
• What relationships matter?
• What performance requirements exist?

Then build a view that provides exactly what the agent needs, nothing more, nothing less.

Join Data Across Systems

Use Pylar's cross-database join capability to unify data:

-- Customer 360 View
SELECT 
  h.customer_id,
  h.customer_name,
  h.email,
  h.company_name,
  h.deal_stage,
  s.order_count,
  s.total_revenue,
  s.last_order_date,
  z.open_tickets,
  z.last_ticket_date,
  st.subscription_status,
  st.plan_name,
  st.mrr
FROM hubspot.customers h
LEFT JOIN snowflake.order_summary s 
  ON h.email = s.customer_email
LEFT JOIN zendesk.ticket_summary z 
  ON h.email = z.customer_email
LEFT JOIN stripe.subscriptions st 
  ON h.email = st.customer_email
WHERE h.is_active = true;

This view gives agents complete customer context from all systems in one query.

Normalize Schemas

Map different schemas to consistent formats:

-- Normalized Customer View
SELECT 
  COALESCE(c.id, u.user_id) as customer_id,
  COALESCE(c.name, u.full_name, CONCAT(u.first_name, ' ', u.last_name)) as customer_name,
  COALESCE(c.email, u.email_address, u.user_email) as email,
  COALESCE(c.state, u.region, u.location_state) as state,
  COALESCE(c.signup_date, u.created_at, u.registration_date) as signup_date
FROM postgres.customers c
FULL OUTER JOIN snowflake.users u 
  ON c.email = u.email_address;

This view normalizes different field names and formats into a consistent schema.

Optimize for Performance

Pre-aggregate and filter data for fast queries:

-- Optimized Sales Pipeline View
SELECT 
  deal_id,
  deal_name,
  customer_name,
  deal_stage,
  deal_value,
  probability,
  expected_close_date,
  days_in_stage,
  owner_name
FROM deals
WHERE deal_stage NOT IN ('closed-won', 'closed-lost')
  AND expected_close_date >= CURRENT_DATE()
  AND deal_value > 1000
ORDER BY expected_close_date ASC;

This view filters out closed deals, low-value deals, and past dates, so agents only query relevant data.

Control Access

Define exactly what data agents can access:

-- Agent-Accessible Customer View
SELECT 
  customer_id,
  customer_name,
  email,
  signup_date,
  subscription_status,
  total_revenue,
  order_count
FROM customers
WHERE is_active = true
  AND signup_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 2 YEAR)  -- Compliance: only last 2 years
  -- Excludes: credit_card_number, internal_notes, ssn, etc.

This view excludes sensitive data and limits data retention for compliance.

Creating MCP Tools from Views

Once you have views, create MCP tools that agents can use. Pylar lets you build tools with AI or manually.

AI-Powered Tool Creation

Use natural language to create tools:

Prompt: "Create a tool to get customer information including order history and support tickets"

Pylar generates: get_customer_info(customer_email: string)

The tool queries your unified customer view and returns complete customer context.

Manual Tool Configuration

For more control, configure tools manually:

Tool Name: get_high_value_customers

Description: "Returns customers with revenue above threshold"

Parameters:

• min_revenue (number, optional): Minimum revenue threshold (default: 10000)
• min_orders (number, optional): Minimum order count (default: 5)

SQL Query:

SELECT * FROM high_value_customers_view
WHERE total_revenue >= {min_revenue}
  AND order_count >= {min_orders}
ORDER BY total_revenue DESC
LIMIT 100;

Multiple Tools Per View

Create different tools on the same view for different use cases:

• get_customer_by_email: Get customer by email address
• get_customer_by_id: Get customer by ID
• search_customers: Search customers by name or company
• get_customer_orders: Get customer's order history

All tools query the same view but with different parameters and purposes.

Monitoring Agent Behavior with Evals

Once agents are using your views and tools, monitor their behavior with Pylar's Evals system.

What Evals Show You

Success Rates: How often queries succeed vs. fail. Low success rates indicate problems with views or tools.

Error Patterns: What errors occur and why. Common errors might indicate schema mismatches or permission issues.

Query Shapes: What types of queries agents are making. This helps you optimize views for common patterns.

Raw Logs: Every query with full context. Use this to debug issues and understand agent behavior.

Using Evals to Improve Views

Based on evals insights, you can:

Optimize Performance: If agents frequently query by email, add an index on email. If queries are slow, pre-aggregate data.

Fix Schema Issues: If agents get "column not found" errors, update views to include missing columns.

Expand Access: If agents frequently can't find data, expand views to include more information.

Refine Tools: If agents misuse tools, update tool descriptions or parameters to guide correct usage.

The Path to Success

If you're building AI agents, here's how to avoid the common failure patterns:

1. Start with Views, Not Raw Access

Before connecting agents to databases, create views that define exactly what data agents need. Start narrow—include only the columns and rows agents actually need. You can always expand later.

2. Unify Data Across Systems

Use Pylar's cross-database join capability to create unified views. Join data from HubSpot, Snowflake, Postgres, and more in a single view. Agents get complete context without querying multiple systems.

3. Normalize Schemas

Create views that normalize different schemas to consistent formats. Map email_address to email, standardize values, and create consistent interfaces. Agents don't need to understand your specific schema—they query normalized views.

4. Optimize for Performance

Pre-aggregate and filter data in views. Agents query fast, optimized views instead of slow, raw tables. Add indexes, set limits, and cache results as needed.

5. Control Access Through Views

Views are the only access level. Agents can only query through views, so you have complete control over what data is accessible. Exclude sensitive data, limit data retention, and enforce compliance requirements.

6. Monitor and Iterate

Use Pylar's Evals to monitor agent behavior. Identify problems, optimize views, and refine tools based on real usage. Iterate continuously to improve agent performance.

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Most AI agent projects fail not because of the models, but because agents can't reliably access the right data at the right time. Structured data views solve this problem by providing a unified, normalized, optimized, and controlled interface to data.

If you're building AI agents, start with views, not raw database access. Your agents will thank you, and your projects will succeed.