How to Handle Real-Time Context Engineering for AI Agents Using Typedef

Core Platform Architecture for Inference-First Operations

Semantic Operators Transform Context Engineering

Fenic revolutionizes context management through eight core semantic operators that function as DataFrame primitives:

• semantic.extract - Transforms unstructured text into structured data using Pydantic schemas
• semantic.join - Enables joining DataFrames based on meaning rather than exact values
• semantic.predicate - Creates natural language filters for context-aware data selection
• semantic.reduce - Aggregates grouped data using LLM operations for intelligent summarization
• semantic.map - Applies natural language transformations to adapt context
• semantic.with_cluster_labels - Groups similar content automatically

The platform provides sophisticated session configuration for hierarchical context management. Developers can configure multiple language models with specific rate limits and token budgets, including support for Claude’s thinking tokens, GPT-4 variants, and Gemini models. This multi-provider architecture includes:

• Automatic failover between models
• Self-throttling mechanisms
• Intelligent model routing based on task requirements
• Token budget management per operation

Native unstructured data types elevate markdown, transcripts, and JSON to first-class citizens in the data pipeline. The framework automatically handles:

• Document chunking with configurable overlap
• Semantic boundary preservation through structure-aware segmentation
• Transcript processing with speaker identity and timestamp retention
• Markdown extraction preserving document hierarchy

Real-Time Capabilities Through Decoupled Architecture

The platform’s decoupled inference architecture separates heavy batch processing from real-time agent interactions. This design enables:

• Responsive agent systems without sacrificing thoroughness
• Async I/O with concurrent request batching
• Built-in retry logic and rate limiting
• Query optimization that understands LLM operations natively

The query optimizer automatically:

• Batches API calls across multiple rows
• Caches repeated inference patterns
• Reduces costs by up to 100x compared to naive implementations

Dynamic context assembly allows agents to build relevant context on-demand through semantic operations. Real-time semantic filtering through semantic.predicate ensures agents only process relevant information, dramatically improving response quality and reducing token consumption.

Context window management becomes automatic through:

• Intelligent batching and optimization
• Token limits as first-class constraints
• Automatic document chunking while maintaining semantic coherence
• Context summarization to maximize information density
• Dynamic context updates as new information becomes available

Implementation Patterns for Production Readiness

MCP Integration for AI-Assisted Development

Fenic’s 0.4.0 release introduced a built-in Model Context Protocol (MCP) server that transforms how AI assistants understand and work with the platform. The self-hosted MCP server configuration allows Claude Desktop and other AI assistants to:

• Access complete Fenic API documentation in real-time
• Understand usage patterns from actual implementations
• Debug issues with codebase knowledge
• Provide context-aware code suggestions

python
# MCP Server Setup for Real-Time Context Assistance
from fenic.api.mcp.server import create_mcp_server
from fenic.api.mcp.tools import SystemToolConfig
import fenic as fc

# Assuming you have a session and tables set up
session = fc.Session.get_or_create(fc.SessionConfig(app_name="mcp_demo"))

# Create MCP server with system tools
server = create_mcp_server(
    session=session,
    server_name="Fenic Documentation Server",
    system_tools=SystemToolConfig(
        table_names=session.catalog.list_tables(),
        tool_namespace="fenic",
        max_result_rows=100
    )
)

This integration enables AI assistants to directly understand Fenic’s semantic operations, making development faster and reducing the learning curve for teams adopting the platform.

Advanced Context Aggregation Patterns

Real-time context engineering requires sophisticated aggregation patterns that Fenic handles elegantly. The platform’s structured extraction capabilities allow developers to define complex context schemas:

python
# Context-Aware Agent Pipeline
from pydantic import BaseModel, Field
from typing import List
import fenic as fc
from fenic.api.functions import semantic

class AgentContext(BaseModel):
    relevant_facts: List[str]
    user_intent: str
    conversation_history: List[str]
    available_actions: List[str]
    confidence_score: float = Field(description="Relevance confidence 0-1")

agent_pipeline = (df
    .semantic.join(
        other=knowledge_base,
        predicate="Is this knowledge relevant to: {{left_on}}?",
        left_on=fc.col("user_input"),
        right_on=fc.col("knowledge_text")
    )
    .with_column(
        "agent_context",
        semantic.extract(
            fc.col("combined_context"),
            AgentContext,
            model_alias="claude",
            max_output_tokens=4000
        )
    )
    .unnest("agent_context")
    .group_by("session_id")
    .agg(
        semantic.reduce(
            "Consolidate context for agent decision making",
            fc.col("relevant_facts")
        ).alias("consolidated_context")
    )
)

The framework provides:

• Intelligent batching - Automatic optimization of API calls across multiple rows
• Explicit caching - Persistence of expensive computations for iterative development
• Row-level lineage tracking - Every transformation is traceable, critical for debugging non-deterministic AI pipelines

Production Deployment with Zero Code Changes

Typedef’s platform follows a “develop locally, deploy to cloud instantly” philosophy. The same code runs seamlessly from laptop to production through the SessionConfig system:

python
# Seamless Local-to-Cloud Deployment
import fenic as fc
from fenic.api.functions import semantic

config = fc.SessionConfig(
    app_name="production_agent",
    semantic=fc.SemanticConfig(
        language_models={
            "flash": fc.GoogleVertexLanguageModel(
                model_name="gemini-2.0-flash-lite",
                rpm=300,
                tpm=150_000
            )
        }
    ),
    cloud=fc.CloudConfig(size=fc.CloudExecutorSize.LARGE)
)

# Create session with config
session = fc.Session.get_or_create(config)

# Same pipeline code works locally and in cloud
production_context = (df
    .with_column(
        "extracted_context",
        semantic.extract(
            fc.col("combined_context"),
            context_schema,
            model_alias="flash"
        )
    )
    .unnest("extracted_context")
    .collect()
)

Performance Metrics and Enterprise Readiness

Token Optimization and Cost Tracking

Built-in comprehensive metrics provide visibility into:

• Token usage per operation
• Query performance benchmarking
• Cost tracking across models
• Context window utilization monitoring
• Session-level aggregate performance
• Operator-level fine-grained metrics

Insurance company Matic reported building “semantic extraction pipelines across thousands of policies and transcripts in days not months,” with dramatic cost reductions through intelligent batching. Content companies use the platform for dynamic narrative classification, processing millions of articles with context-aware tagging that adapts to evolving stories.

Scalability Through Intelligent Architecture

The Rust-powered query engine built on Apache Arrow provides:

• Columnar memory layout for efficient processing
• Lazy evaluation for sophisticated query optimization
• Single-node to distributed compute scaling through Ray integration
• Excellent single-node performance due to inference optimization

Real-world deployments demonstrate:

• 100x time savings for semantic extraction tasks
• “Dramatically reduced time to eliminate errors caused by human analysis”
• Significant cost reductions through automatic optimization
• Production reliability with automatic retry logic and comprehensive error handling

Best Practices for Context Engineering Implementation

Structure Extraction from Unstructured Data

Typedef recommends treating unstructured data as containing latent structure that LLMs can surface. Implementation guidelines include:

Leverage Semantic Chunking

• Use intelligent chunking over naive character-count splitting
• Respect document structure for semantically meaningful segments
• Configure overlap to ensure context continuity
• Preserve document hierarchy and temporal relationships

Extract and Preserve Structure

python
# Example: Structure-Aware Document Processing
import fenic as fc
from fenic.api.functions import markdown
from fenic.api.functions.builtin import when

# Note: Fenic doesn't have built-in transcript parsing, so this example
# focuses on markdown processing
df_documents = session.create_dataframe([
    {"content": markdown_document, "type": "markdown"},
    {"content": transcript_text, "type": "text"}
])

# Process markdown documents with header-based chunking
processed = df_documents.with_column(
    "structured_content",
    when(fc.col("type") == "markdown")
    .then(markdown.extract_header_chunks(fc.col("content"), header_level=2))
    .otherwise(fc.col("content"))  # Keep text as-is
)

Dynamic Context Assembly Strategies

Effective context engineering requires adaptive context selection based on query requirements:

Implement Hierarchical Context Management

• Define multiple model tiers (nano, flash, full)
• Set appropriate rate limits and token budgets per tier
• Route queries based on complexity and latency requirements
• Optimize both cost and performance

Use Semantic Operations for Context Selection

python
# Dynamic Context Assembly Based on User Query
import fenic as fc
from fenic.api.functions import semantic

relevant_context = (knowledge_base
    .filter(
        semantic.predicate(
            f"Is this relevant to: {user_query}? Content: {{{{content}}}}",
            content=fc.col("content")
        )
    )
    .with_column(
        "summary",
        semantic.map(
            "Summarize key points relevant to the query: {{content}}",
            content=fc.col("content")
        )
    )
    .limit(context_window_size)
)

Monitoring and Debugging Workflows

The platform’s capabilities enable unprecedented debugging for AI pipelines:

Implement Row-Level Lineage Tracking

• Trace every transformation including non-deterministic outputs
• Investigate unexpected agent behaviors
• Optimize context selection strategies
• Maintain audit trails for compliance

Use Explicit Caching Strategically

python
# Cache expensive operations for iterative development
import fenic as fc
from fenic.api.functions import semantic

df_enriched = (df
    .with_column(
        "extracted",
        semantic.extract(
            fc.col("combined_context"),
            schema,
            max_output_tokens=4000,
            model_alias="gpt-4"
        )
    )
    .unnest("extracted")
    .cache()  # Persist results
    .filter(
        semantic.predicate(
            "Contains actionable insights: {{content}}",
            content=fc.col("combined_context")
        )
    )
)

Advanced Real-Time Context Engineering Patterns

Multi-Stage Context Refinement

Fenic enables sophisticated multi-stage pipelines for context refinement:

python
# Three-Stage Context Refinement Pipeline
import fenic as fc
from fenic.api.functions import semantic
from fenic.api.functions.embedding import compute_similarity

# Assuming df has embeddings already computed
# Stage 1: Find similar items based on embeddings
user_embedding = [0.1, 0.2, ...]  # Your user intent embedding
stage1_broad = (df
    .with_column(
        "similarity",
        compute_similarity(fc.col("content_embedding"), user_embedding, metric="cosine")
    )
    .order_by(fc.desc("similarity"))
    .limit(100)
)

# Stage 2: Filter with semantic predicate
stage2_relevant = stage1_broad.filter(
    semantic.predicate(
        "Directly addresses the user's specific question: {{content}}",
        content=fc.col("content")
    )
)

# Stage 3: Reduce to summary
stage3_refined = (stage2_relevant
    .group_by(fc.lit(1).alias("group"))  # Group all rows
    .agg(
        semantic.reduce(
            "Create a comprehensive context summary",
            fc.col("content"),
            model_alias="claude-sonnet"
        ).alias("refined_context")
    )
)

Agent Memory Management

Real-time agents require sophisticated memory management that Typedef’s platform handles through:

• Short-term memory - Current conversation context
• Long-term memory - Persistent knowledge across sessions
• Working memory - Task-specific context assembly
• Episodic memory - Historical interaction patterns

python
# Agent Memory System Implementation
import fenic as fc
from fenic.api.functions import semantic

class AgentMemory:
    def __init__(self, session):
        self.session = session
        self.short_term = session.create_dataframe({"content": [], "timestamp": []})
        self.long_term = session.create_dataframe({"content": [], "timestamp": []})

    def update_context(self, new_input):
        # Update short-term memory using union
        new_df = self.session.create_dataframe([new_input])
        self.short_term = self.short_term.union(new_df)

        # Consolidate to long-term when needed
        row_count = self.short_term.count()
        if row_count > threshold:
            consolidated = (self.short_term
                .group_by(fc.lit(1).alias("group"))
                .agg(
                    semantic.reduce(
                        "Extract key facts for long-term storage",
                        fc.col("content")
                    ).alias("consolidated_content")
                )
            )
            self.long_term = self.long_term.union(consolidated)
            self.short_term = self.session.create_dataframe({"content": [], "timestamp": []})

Declarative Tool Integration

The latest Fenic release introduced declarative tool support for seamless agent-tool interactions:

python
# MCP Tool Integration with Fenic
from fenic.api.mcp.server import create_mcp_server
from fenic.api.mcp.tools import SystemToolConfig
import fenic as fc

# Create session
session = fc.Session.get_or_create(fc.SessionConfig(app_name="agent_tools"))

# Save knowledge base as a table
knowledge_df.write.save_as_table("knowledge_base", mode="overwrite")
session.catalog.set_table_description("knowledge_base", "Internal knowledge base for agent queries")

# Create MCP server with system tools that can query the knowledge base
server = create_mcp_server(
    session=session,
    server_name="Agent Tool Server",
    system_tools=SystemToolConfig(
        table_names=["knowledge_base"],
        tool_namespace="agent",
        max_result_rows=5
    )
)

# The MCP server now provides tools like:
# - agent_schema: List columns/types of knowledge_base
# - agent_read: Read rows from knowledge_base with filters
# - agent_analyze: Run SQL queries on knowledge_base

Conclusion

Typedef.ai’s Fenic platform represents a paradigm shift in AI infrastructure, treating context engineering as a fundamental concern rather than an afterthought. By combining inference-first architecture with familiar DataFrame abstractions, the platform enables teams to build sophisticated, production-ready AI agents with unprecedented efficiency. The open-source foundation ensures continuous innovation while the commercial Typedef Cloud platform provides enterprise-grade scalability and support.

With semantic operators as first-class citizens, automatic context window management, and seamless local-to-cloud deployment, Fenic provides the comprehensive toolkit needed for real-time context engineering in modern AI applications. As teams increasingly recognize that 87% of enterprise AI projects fail to reach production, Typedef’s approach of bringing deterministic structure to non-deterministic models offers a clear path forward for operationalizing AI at scale.

For teams ready to transform their AI infrastructure, explore the Fenic framework or learn more about Typedef’s platform.