Context Engineering for AI Agents: What Comes After Prompt Engineering

The discipline that separates production AI agent systems from demos has a name: context engineering. If you have been building LLM applications, you know that prompt engineering — crafting the right instructions — gets you started. Context engineering is what gets you to production.

The clearest evidence comes from Bayer’s PRINCE system, documented by Martin Fowler in June 2026. Bayer built an AI system to help researchers access preclinical data. The team’s most important finding: retrieval architecture mattered more than prompt quality. The key was not writing better prompts. It was controlling exactly what information each agent received.

What Context Engineering Actually Means

Context engineering is the discipline of controlling what information reaches each agent in a multi-agent system. It has four components:

  1. Retrieval control — which documents, database rows, or API results go into the context window
  2. Memory management — what the agent remembers across turns, with what TTL
  3. Tool selection — which tools are available to this agent at this step, not all of them
  4. Handoff design — what information one agent passes to the next, and what it withholds

The Bayer PRINCE architecture combines RAG for unstructured documents and Text-to-SQL for structured database queries. The orchestration layer routes each query to the right data source, and critically — each agent receives only the context it needs. A summarization agent does not see raw SQL results. A validation agent does not see the full document corpus. This is context engineering.

The 3 Failure Modes Context Engineering Prevents

Context Pollution

When an agent receives irrelevant information, it makes errors by hallucinating connections between unrelated pieces of context. The solution is to filter at the retrieval layer, not at the prompt level.

# Bad: give the agent everything and hope it ignores what is irrelevant
context = fetch_all_documents(query)  # 50 documents, most unrelated

# Good: filter before sending to the agent
relevant_docs = semantic_search(query, k=5, threshold=0.75)
context = format_for_agent(relevant_docs)

Context Overflow

LLMs have token limits. When you exceed them, the model either errors or silently truncates — both are bad in production. The solution is to budget context windows explicitly.

def build_agent_context(docs, history, tools, max_tokens=12000):
    TOOL_BUDGET = 2000
    HISTORY_BUDGET = 3000
    RESERVE = 1000  # for system prompt, response
    docs_budget = max_tokens - TOOL_BUDGET - HISTORY_BUDGET - RESERVE

    compressed_docs = truncate_to_budget(docs, docs_budget)
    return AgentContext(docs=compressed_docs, history=history, tools=tools)

Context Stale

When agents remember outdated information across turns, they make decisions based on state that no longer exists. The solution is explicit memory management with TTL and relevance scoring.

class AgentMemory:
    def store(self, key, value, ttl_seconds=300):
        self.memory[key] = {
            "value": value,
            "expires_at": time.time() + ttl_seconds,
            "relevance": self.score_relevance(value)
        }

    def retrieve(self, query):
        return [
            m["value"] for m in self.memory.values()
            if m["expires_at"] > time.time()
            and m["relevance"] > 0.6
        ]

Harness Engineering: The Other Half

Context engineering pairs with harness engineering — the infrastructure that orchestrates agents, handles errors, and provides observability. The harness is what makes an agent reliable enough for production.

class AgentHarness:
    def run_with_retry(self, agent, context, max_retries=3):
        for attempt in range(max_retries):
            try:
                result = agent.run(context)
                self.log_context_and_result(context, result)
                if self.validate(result):
                    return result
                context = self.reduce_context(context)  # simplify on retry
            except Exception as e:
                self.log_failure(e, context)
                if attempt == max_retries - 1:
                    raise
        return self.fallback_result(context)

Key harness responsibilities:

  • Error recovery: retry with simplified context when an agent fails
  • Observability: log exactly what context each agent received, not just its output
  • Guardrails: validate agent output before passing it downstream

From the PRINCE Architecture: Lessons That Apply Everywhere

Three principles from the Bayer system transfer to any agentic application:

Principle 1: Route before retrieving. Determine what type of query it is — document lookup vs database query vs calculation — before you pull context. The wrong retrieval path wastes tokens and introduces noise.

Principle 2: Give agents narrower context than you think they need. Start minimal and add context when the agent fails, rather than starting broad and hoping the agent ignores irrelevant information. LLMs are better at using focused context than ignoring noise.

Principle 3: Log context, not just outputs. When an agent produces a wrong answer, the answer itself rarely tells you why. The context it received does. Your observability stack needs to capture what went into the agent, not just what came out.

What This Means for Your Team in 2026

If you are building or evaluating AI agent systems:

  1. Treat context as a first-class engineering concern. Assign ownership of the retrieval and memory architecture, the same way you would assign ownership of a database schema.

  2. Instrument your context windows. Know how full each agent’s context is before production failures teach you the hard way. Alert when context budget exceeds 80%.

  3. Design for the 95th percentile query. What happens when a user asks something that pulls in 10 times the normal amount of context? That is the case that breaks production.

  4. Test your harness, not just your agents. Unit tests for individual agents miss harness-level failures — what happens when two agents disagree, or when the retrieval system returns empty results.

Context engineering is the discipline that separates production AI from demo AI. The teams shipping reliable agent systems in 2026 are not the ones with the best prompts. They are the ones who engineered the information flow.

Export for reading

Comments