Beyond RAG
Context Engineering for Company-Brain Agents
The first version usually looks like this
Documents
→
Chunks
→
Embeddings
→
Vector DB
→
Retrieved context
→
LLM answer
- Fast to prototype
- Impressive in demos
- Useful for semantic recall
- Often the starting point
Why naive RAG breaks in production
- Retrieval ambiguity
- Stale or conflicting knowledge
- Semantic similarity is not task relevance
- Chunking destroys context
- No workflow awareness
- Difficult to evaluate correctness
Relevant-looking context is not the same as correct context.
Composite case: architectural compliance assistant
A domain-specific assistant for architectural and building-code compliance.
User asks
Does this design satisfy the relevant constraints?
System requirements
- Regulations
- Project metadata
- Design constraints
- Jurisdiction-specific rules
- Traceable evidence
Lesson: Compliance AI is structured reasoning over rules and evidence, not generic Q&A.
From RAG to agentic workflows
- Retrieval becomes one tool among many
- Agents can:
- Plan
- Retrieve iteratively
- Call APIs
- Use MCP-compatible tools
- Validate intermediate outputs
- Delegate subtasks
The agent should not just answer. It should execute a workflow.
User query
↓
Supervisor agent
↓
Specialist agents
↓
Tools / APIs / retrieval
↓
Answer + citations
Composite case: agentic e-commerce analytics
A large-scale analytics platform where users ask questions about market share, pricing, trends, and brand performance.
Architecture
- Supervisor agent routes questions
- Specialist agents analyze subproblems
- MCP tools access backend systems
- Synthesis agent produces final answer with citations
Evals become infrastructure
Reliability becomes the real problem
- Benchmark questions run continuously
- Ground-truth answers are maintained
- Systems check:
- Numerical correctness
- Citation correctness
- Backend grounding
- Reasoning consistency
- LLM-as-a-judge complements deterministic checks
Insight: The challenge shifts from generating answers to measuring reliability.
What context engineering really means
Context engineering is deciding what information should enter the model context at each step.
Include
- Retrieved documents
- Tool outputs
- User intent
- Workflow state
- Memory
- Constraints
- Evidence and citations
Distinction: Not all state belongs in the prompt. Not all memory belongs in vector search.
The architecture moves outside the model
- The LLM is only one component
- Orchestration matters
- Tools matter
- Evals matter
- State management matters
- Context engineering becomes the core challenge
The hard part is no longer making the model answer. The hard part is making the answer trustworthy.