Agent Context and Caching

## Problem

The current agent stack works, but it is too prompt-string heavy and too expensive for long-lived use:

- Chat injects every entity type schema into the prompt on every turn.
- Entity-scoped chat trusts client-supplied entity context instead of reloading the latest server truth.
- Chat, workflow, extraction, heartbeat, and inbox each assemble context differently.
- Stable instructions and tool definitions are not provider-cache aware.
- Long conversations have no provider-level context management strategy.
- Shared context and memories can grow without meaningful prompt budgeting.
- Agents are not given a structured tenant-authored business profile, so OCI-specific knowledge depends on whatever happens to exist in records or prior conversations.

This leaves quality, latency, and cost on the table.

## Goals

1. Create a shared context pipeline that all agent entry points can reuse.
2. Keep stable context rich, but compact enough to avoid wasting prompt budget.
3. Add tenant-level business profile support so OCI and future clients can author durable business context once and inject it everywhere.
4. Add provider-aware caching and context management for Anthropic-backed agents.
5. Improve prompts and tool instructions around retrieval-first behavior, freshness, citations, and value framing.
6. Keep the implementation platform-safe: no product-specific slugs in platform code.

## Design

### Shared Context Layers

Prompts should be assembled from distinct layers with different stability and cost profiles:

1. **Stable cached context**
   - Base system prompt
   - Tenant-authored workspace business profile
   - Compact data-type summary
   - Stable shared-context guidance (corrections, lessons, routing)
2. **Dynamic session context**
   - Current date/time
   - Current user and role
   - Active entity context
   - Recent bounded memories
3. **Execution-local context**
   - Workflow node instructions
   - Extraction field instructions
   - Heartbeat attention context

### Workspace Business Profile

Add a tenant setting key, `agent_context`, with a structured schema:

```ts
{
  summary: string;
  industry?: string;
  businessModel?: string;
  customers?: string[];
  valueDrivers?: string[];
  coreProcesses?: string[];
  keySystems?: string[];
  successMetrics?: string[];
  terminology?: string[];
  constraints?: string[];
  currentPriorities?: string[];
  differentiators?: string[];
}
```

This becomes the durable place to encode what OCI actually does, how it makes money, which processes matter, which systems it relies on, and how success should be judged.

### Prompt Compaction

Replace raw full-schema injection with a compact type summary:

- show type name, slug, description, active fields, and relation labels
- cap fields/relations per type
- highlight the focused type when a chat is scoped to a record
- instruct agents to use `listEntityTypes({ detailed: true })` when they need the full schema

Entity context should also be compacted:

- summarize field values as named bullets
- cap field count and value length
- avoid dumping large JSON blobs into the system prompt

### Runtime Improvements

Enhance the shared runtime to support:

- deterministic tool ordering
- Anthropic cache control on stable system/tool definitions
- Anthropic context management for long chats
- pass-through `toolChoice` and `prepareStep` so callers can tighten agent loops when needed

### Safety Hardening

Chat should no longer trust client-supplied entity title/type/content as system-prompt input.
If an entity ID is provided, the route should reload the latest tenant-scoped entity context from the server and only inject that verified version.

## Acceptance Criteria

- [ ] Chat, workflow, heartbeat, inbox, and extraction all can consume the same workspace business profile prompt
- [ ] Chat uses server-resolved entity context instead of trusting client payload fields
- [ ] Entity type prompt section is compact and bounded
- [ ] Shared context and memories are bounded for prompt size
- [ ] Anthropic runs receive cache-control breakpoints on stable system/tool definitions
- [ ] Anthropic chat runs receive context-management settings for old tool-use cleanup
- [ ] Runtime exposes `toolChoice` / `prepareStep`
- [ ] System-agent prompts are updated for retrieval-first behavior, citations, and value framing
- [ ] OCI tenant seed populates `agent_context`
- [ ] Tests cover the new workspace-context and provider-options behavior

## Rollout Notes

- `agent_context` is additive and backward-compatible. Workspaces without it continue to run with existing prompts.
- Anthropic caching/context-management should only be applied when the resolved model ID is Claude-family.
- Tool ordering must remain deterministic so prompt caching gets reuse from identical tool definitions.