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AI Agent Context Management
Agent Context Management: The Control Plane for AI Agents
Agent context management controls what AI agents retrieve, use, cite, revoke, and audit through identity checks, entitlement rules, and audit trails.
Contributing author
Karthik Pasupathy
Contributing Writer — AI Context & Agents
Karthik Pasupathy is a product marketer and the founder of Rampkit, with over a decade spent helping B2B SaaS and AI-infrastructure companies explain complex products to enterprise buyers and practitioners. He has led marketing for early-stage companies across FinTech, CCaaS, and mortgage technology. At Atlan, he writes on AI context, agentic systems, and RAG metadata.
/ Profile
About Karthik.
Karthik Pasupathy is a technical writer turned product marketer and founder of Rampkit, where he helps technical B2B SaaS and AI infrastructure companies explain complex products to the people they are built for. His work sits at the intersection of content, product understanding, and market education — turning dense technical ideas into clear narratives for enterprise buyers, practitioners, and technical decision-makers.
Over the last decade, Karthik has worked with SaaS companies across AI infrastructure, DevOps, APIs, FinTech, CCaaS, mortgage technology, and enterprise software. His writing focuses on helping teams communicate why their product matters, how it fits into a larger technical workflow, and what problems it solves for specific user personas.
His current work explores AI context, agentic systems, RAG metadata, and how enterprises can make AI outputs more relevant, grounded, and useful. He brings a content-first lens to technical topics without stripping away the nuance that makes them valuable.
Press, talks & code
Recent articles by Karthik Pasupathy.
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- 02Multi-Agent Context Management
Context Management for Multi-Agent Systems: Context Patterns
Learn how to manage shared context across multi-agent systems using governed context stores, scoped sub-agents, conflict rules, and refresh cycles.
- 03AI Agent Context
How to Manage Long-Term Context in AI Agents
Learn how long-term context management works for AI agents, where memory layers fall short, and how Context Repos keep enterprise context governed.
- 04Context Engineering
Context Engineering Techniques for AI Agents: Which One to Use
Atlan's guide to context engineering techniques for AI agents: diagnose failures, choose the right fix, and know when source context is the real problem.
- 05Context Layer
Context Layer Evaluation Criteria: Real vs. Relabeled Catalog
Understand the criteria that separate a real context layer from a relabeled catalog: model-agnostic delivery, ownership, provable correctness, and audit.
- 06AI Agent Cost and TCO
How Much Does It Cost to Run AI Agents at Scale?
Learn what it costs to run AI agents at scale in 2026: why token spend rises, what belongs in agent TCO, and how a shared…
- 07AI Agent Operations
Enterprise-Ready AI Agents: What Production Trust Requires
Learn what makes enterprise-ready AI agents accurate, trusted, governed, and scalable in production, and why context matters beyond the agent harness.
- 08AI Agents
How Enterprises Use AI Agents: Use Cases That Stick
How are enterprises using AI agents? Explore real use cases, adoption numbers by industry, and why most agentic AI projects stall before reaching scale.
Transparency
Editorial relationship
Freelance contributor agreement
Karthik contributes to Atlan as an independent consultant under a freelance content agreement. He reviews and approves every article published under his byline before publication. Atlan does not pay for placement of any external coverage.
Reach Karthik