Context Engineering: The Secret to High-Performing Agentic AI

Key Takeaways

• Context engineering ensures agentic AI performs reliably in complex, regulated environments by delivering just the right information at the right time.
• Prompt engineering alone cannot substitute for structured, lifecycle-managed context environments that evolve with user feedback and operational needs.
• AgentFlow platform implements context engineering through multi-layer architecture, SME collaboration, and post-deployment tuning.
• The three pillars—operationalized knowledge, context infrastructure, and deployment strategy—guarantee that context is captured, persisted, and improved over time.
• Insurance and finance customers have reached over 95% task accuracy using context engineering practices that align with their workflows and compliance requirements.
• Tools like schema builders, JSON logs, and Progress Tracker bridge the gap between business users and AI agents, ensuring consistent output and traceability.

What Is Context Engineering?

Context engineering is the practice of deliberately constructing and maintaining the information environment an AI agent uses to perform a task. It ensures the underlying LLM sees all the relevant information and none of the irrelevant information. 

This includes everything from retrieved documents, structured data, and external data to outputs from related tasks.

The 3 Pillars of Context Engineering

1. Operationalized Knowledge

• SMEs map real-world processes, configuring AI agents with natural language prompts.
• Schema builders define document formats and validation rules.
• Prebuilt templates encode 80% of the domain logic for claims, loans, and underwriting.

2. Context Infrastructure

• Multi-agent orchestration maintains a structured context across tasks.
• Embeddings, memory, and vector store preserve and retrieve relevant memories.
• Audit trails log confidence scores, decisions, and tool calls for traceability.

3. Deployment Strategy

• VPC or on-prem deployments ensure context isn’t lost to third-party tools.
• Feedback loops evolve logic via user feedback and usage data.
• AgentFlow’s Progress Tracker connects AI actions to business SOPs.

The Three Pillars of Context Engineering

Why Context Engineering Matters for Agentic AI

In an agentic system, each agent relies on a structured context to work with other agents, tools, and data sources. Without context, you get hallucinations, inconsistent outputs, or tasks that stall out due to lack of understanding. 

Context matters because:

• Token limits force hard decisions about what the model sees.
• Different agents work on different aspects of a process; they must exchange context.
• Each system prompt, user input, and tool call changes what context is active.

High-performing agents don’t rely on a single prompt. They execute decisions based on structured outputs, persistent long-term memory, and dynamic systems for adapting context in real-time.

How Context Engineering Boosted Agentic AI: Insurance Underwriting Example

Take our insurance underwriting deployment case study. Here, context engineering was the difference between demo-grade agents and production-grade results.

Schema Definition + SME Collaboration

We created separate solutions for every document type our client has to handle. This involved SME-guided schema creation tailored for each insurer, essentially defining schemas and desired outputs per insurer. 

That’s applying context engineering at the business configuration layer, ensuring the AI knows what relevant data looks like, where to find it, and how to structure it.

Template-Based Logic

We embedded three different templates for three different insurance companies, each reflecting an insurer’s unique rules like specific fields, formats, and risk-relevant data. All are essential to context engineering. 

This is a structured context, not a static prompt. It gives the AI additional context needed to reason across jurisdictions and formats.

Complex Format Handling

Our tool selection included a computer vision model to link text fields to visual anchors. That’s building AI agents that can handle real-world messiness—a key part of the delicate art and science of context engineering.

Feedback and Accuracy Improvement

Iterative SME feedback tuned performance to >95% accuracy. This is how context evolves: via deployment, user query logs, user feedback, and re-training cycles.

Our Approach to Context Engineering

The Context Engineering Stack Behind AgentFlow

AgentFlow structures context into layers:

• Business Configuration Layer: SMEs define schemas, workflows, and approval logic.
• Technical Configuration Layer: IT sets access control, monitoring, and deployments.
• Built-in tools include:
  
  • Schema/workflow builders
  • Confidence scoring
  • Execution logs
  • Drift detection

This isn’t just a wrapper over an LLM. It’s an industrial-strength LLM app built for production.

Collaborative Configuration With SMEs and IT

We embed with subject matter experts and IT teams to encode real-world logic into AI workflows. This collaboration ensures that agents understand and act according to business rules, compliance constraints, and edge scenarios.

Our teams work alongside underwriters, claims handlers, and analysts to:

• Label edge cases directly within documents and workflows
• Define schema structures and domain-specific rules
• Tune confidence thresholds for automatic approvals or human escalation
• Review execution logs to refine tool use, query formats, and decision logic

This structured process turns tribal knowledge into repeatable, governed AI behavior. It’s not a one-time setup.

How Context Evolves After Deployment

Our clients use AgentFlow’s tools to:

• Coach AI Agents through human-in-the-loop feedback mechanisms
• Conduct A/B testing on context configurations
• Use the Progress Tracker to align decisions to SOPs
• Track every step in JSON logs

Context engineering doesn’t stop once an agent is deployed. It continues through monitoring, retraining, and tuning to ensure the AI adapts as the business evolves.

Does This Mean Prompt Engineering Is Dead?

No, but it’s no longer enough. The term context engineering captures what prompt engineering can’t. Let’s break it down.

• Prompt engineering: crafting a system prompt or user input to get the LLM to respond in a certain way.
• Context engineering: shaping what the model knows by managing short-term memory, long-term memory, retrieved documents, schema constraints, and outputs from symbolic AI or tools.

A recent research paper confirms that context compression, context selection, and context formatting are central to enterprise LLM performance.

People associate prompts with clever tricks. But we operate in regulated environments where precision, auditability, and structure come first. So we engineer the right context instead.

What Makes Our Context Engineering Work?

We Understand Format Matters

The format matters as much as the content. Structured outputs, JSON logs, and schema-aligned responses ensure the model sees the task, not just the words.

We Track Every Tool Call and Step

Each step of the process—from retrieval augmented generation to tool calls and downstream agentic systems—is tracked, logged, and available for replay.

We Think From First Principles

We ask What does the AI need to plausibly accomplish the task? Then we build a context stack that includes:

• Semantic search over vector databases
• Context compression to maximize context window use
• Web search integration for external information
• Few-shot examples that clarify edge behavior

Book a Meeting With Our Team

AgentFlow was built from the ground up to support effective context engineering. Whether you’re underwriting policies, adjudicating claims, or processing loans, our agents know what matters because they see the full picture. Not just a single prompt, but all the context your workflows require.

Want to see how this works in practice? Book a demo, and we’ll walk you through the stack.

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