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Overview
As AI models converge on strong reasoning capabilities, the differentiator for effective
agentic systems is increasingly not the model itself, it's the context you provide it.
Context engineering — the discipline of delivering the right information, at the right
granularity, at the right time — is becoming a core competency for teams building agents
that work reliably in production. In this talk, we define the problem, share practical
principles for context architecture, and walk through how we've approached it with our
own products.
Outline
I. The Context Problem
- As agents take on longer, multi-step tasks with real-world consequences,
the quality of their context becomes the primary driver of output quality
- Most agent failures in practice aren't reasoning failures — they're context
failures: the model was missing information, working with outdated
information, or overwhelmed by irrelevant information
- Context engineering as a discipline: distinct from prompt engineering,
closer to systems design. It's about pipelines, infrastructure, and
information architecture — not just what goes in the system prompt.
II. What Good Context Looks Like
- Relevance is the measure that matters – The goal of context engineering
isn't to maximize information available to the model — it's to maximize
relevance. Everything else follows from this.
- You can't always predict relevance in advance – Agentic workflows are
dynamic. What's relevant at step five depends on what the agent discovered
at step three. Pre-curated context assumes you know the path before the
agent walks it.
- Explorability is how you achieve relevance at scale – Rather than
pre-identifying the right context, the more resilient approach is making your
knowledge base easy for the agent to traverse and explore on the fly. The
design question shifts from "build a better retriever" to "make your
knowledge navigable."
- This is where most teams are under-investing – The default
embed-index-retrieve pattern optimizes for static similarity. It doesn't
support an agent that needs to follow threads across systems, discover
adjacent context, or refine its understanding as a task unfolds.
- Measure whether agents are finding what they need – Without this, you
can't tell whether a bad output is a reasoning problem or a context problem.
III. Turning Enterprise Knowledge into Agent-Ready Context
- Enterprise environments are where context engineering is both most
difficult and most impactful — the knowledge exists but it's scattered,
siloed, inconsistently structured, and constantly changing
- The untapped goldmine: docs, wikis, tickets, CRMs, codebases, Slack
threads — most enterprise knowledge is already there, just not accessible
to agents
- Strategies for ingesting, indexing, and surfacing enterprise knowledge at
inference time
- Handling permissions, access control, and data sensitivity in context
pipelines
- How we achieve this with Kong Context Mesh: a walkthrough of our
approach to transforming enterprise resources into agent-ready context
1/ Our architecture for ingesting, indexing, scoping, and serving
enterprise context at inference time
2/ Assumptions we made early on that turned out to be wrong, and
what we learned
3/ What measurably improved when we got context right, and where
we're still iterating
Speaker Bio
Christopher Tam is the Product GM for Kong’s Agentic AI Infrastructure offerings,
including AI Gateway, Context Mesh, and KAi. A veteran in the AI and infrastructure space,
Chris previously founded Substrates.ai, an agentic infrastructure startup, and held
product leadership roles at Google and Verily, where he applied cutting-edge AI
technologies to business applications. He was also a VP at Leap Motion, an early pioneer
in computer vision.
Speakers 
Kong
Christopher Tam is the Product GM for Kong’s Agentic AI Infrastructure offerings,
including AI Gateway, Context Mesh, and KAi. A veteran in the AI and infrastructure space,
Chris previously founded Substrates.ai, an agentic infrastructure startup, and held
product leadership ro...
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