Knowledge without memory is guesswork. We take a hard look at why most workflow agents stall at triage and show how to turn them into knowledge agents that deliver trusted, context-rich answers drawn from your organisation’s best thinking.
Starting with the real cost of lost information and context switching, we map the path from scattered wikis and chat threads to a reliable institutional memory powered by retrieval augmented generation and hybrid search.
At a Glance / TLDR:
We break down the decisions that matter: how to chunk documents so the agent can both recall facts and reason across context, how to enrich content with metadata that signals authority and freshness, and how to fuse vector semantics with keyword precision for queries that mix intent with exact terms like product codes and financial acronyms.
On the engineering side, we cover architecture trade‑offs between vector databases and native hybrid search, secure connectors into CRM and ERP systems, and the governance needed for citations, audits, accuracy monitoring, and content freshness.
You’ll hear where teams slip - capacity spikes, weak document prep, brittle identity integrations - and how to design for elasticity and compliance from day one.
The proof is in production. Uber’s engineering co‑pilot reclaimed thousands of hours and raised answer quality; JP Morgan Chase scaled insights to more than two hundred thousand employees and unlocked major business value; Goldman Sachs is pushing beyond retrieval to application, where the agent drafts, analyses, and accelerates financial workflows.
Across these stories, a shared blueprint emerges: executive sponsorship, domain expert curation, user‑centred iteration, and workflow redesign that embeds the agent into daily decisions. If you’re ready to turn proprietary knowledge into a real moat and to build a platform that compounds value across use cases this conversation offers the playbook.
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