Agentic AI & RAG

Context engineering for agentic AI systems

An event tells an agent that something happened. Context tells it what that actually means, and without context, agents are fragile.

CAPTIVOLT INSIGHTS · Published 13 August 2026 · Updated 13 August 2026 · 4 min read

Executive summary

AI agents should not act on a single prompt, event, or API response. In real business workflows, an agent needs to understand the bigger picture before it acts: what happened, where it sits in the workflow, which business rule applies, what knowledge and tools are available, whether confidence is high enough, and whether a human must approve. Engineering that surrounding context is what separates a fragile demo agent from a reliable production one.

The problem

A raw event (“payment failed”, “approval status changed”, “document uploaded”) carries almost no meaning on its own. An agent that acts on the event alone will misread the situation: it does not know the workflow state, the applicable policy, the related history, or whether it is even allowed to take the action it is about to take. The result is an agent that works in a scripted demo and behaves unpredictably against real business context.

The Captivolt thesis

Context is engineered, not retrieved: assembled for each decision, filtered by the permissions of the person asking before anything is ranked, and handed over with its provenance intact.

A practical framework

01
Enrich the context first. Before the agent reasons, assemble workflow state, user and session data, the business rules that apply, and policy context.
02
Ground it in knowledge and memory. Connect RAG, vector databases, and knowledge bases, plus short- and long-term memory, so the agent reasons over what is actually known.
03
Orchestrate deliberately. Planning, reasoning, confidence checks, and explicit escalation decisions, not a single leap from prompt to action.
04
Gate tool access. Reach enterprise systems through MCP, a tool registry, and an agent gateway, limited to approved actions.
05
Wrap it in governance and observability. Guardrails, audit logs, PII detection, tracing, and model evaluation across the whole flow.
06
Engineer for cost. Semantic caching, prompt compression, model routing, token budgeting, and cost monitoring.

Going further

The assembly pipeline

What happens between an event arriving and an agent reasoning about it, written as engineering steps rather than principles. Each step emits something, and the trace of what each emitted is what makes the agent’s decision explicable afterwards.

Intent resolution

Turn the event into the question it raises: what happened, in which workflow, at which state.

Emits: the resolved intent and the workflow position.

Candidate gathering

Pull from every store the question needs (records, documents, history, policy) rather than from a single index.

Emits: candidates, each with its source and version.

Permission filtering

Apply the caller’s entitlements at query time, before anything is ranked, counted or summarised.

Emits: what was excluded, and why.

Conflict and supersession

Resolve disagreements between sources and discard what a newer version replaced, before the model sees either.

Emits: the conflicts found and how each was resolved.

Budget allocation

Spend a finite context budget deliberately: policy and instructions reserved first, then evidence ranked by relevance and authority.

Emits: what was included, what was dropped, and the budget used.

Packaging with provenance

Hand the agent the assembled context with source, version and classification still attached.

Emits: the context package the agent reasons over.

Confidence and escalation

Signal whether the context is sufficient to act on, and route to a person when it is not.

Emits: a sufficiency signal and, where needed, an escalation.

The six-layer context architecture →

Failure modes specific to context engineering

Distinct from retrieval failures. These are the ways well-retrieved context misleads an agent that would otherwise behave correctly.

Context poisoning

Instructions embedded in retrieved content or a tool response are read as direction.

Control: treat retrieved text as data, and authorise actions by policy rather than by model intent.

Policy starvation

A large evidence payload crowds the governing policy out of the budget.

Control: reserve budget for policy and instructions before any evidence is added.

Stale memory

Long-term memory returns a fact that was true and no longer is.

Control: a retention policy, a named owner, and supersession applied to memory exactly as to documents.

Permission drift

Entitlements change after context was cached, and the cache goes on serving what the user may no longer see.

Control: key caches on the caller’s entitlements and invalidate them when those change.

What an agent does on every run →

Examples

Worked through elsewhere on this site.

Reference flows and published architectures rather than client runs, each described as what it is.

One question, all the way through →
A reference query traced through retrieval, including the documents it was not allowed to read and the sentence it declined to write.
The six-layer context architecture →
What each layer of the context layer is built from, and what it cannot do on its own.

Practical implications

The event tells the agent something happened; context tells it what it means.
Enrichment, knowledge, orchestration, tool access, governance, and cost control are one architecture, not add-ons.
Escalation to a human is a design feature, not a fallback.
Without context, agents are fragile; with it, they are reliable, safer, and genuinely useful.

What leaders should do

Fund the context layer as shared infrastructure, rather than rebuilding retrieval inside each AI project.
Require retrieval to run under the caller’s permissions, and ask how that is tested.
Give long-term memory an owner and a retention policy before an agent is allowed to write to it.
Ask for the context trace behind a decision: what was included, what was excluded, and why.