How do you manage your AI agents' context across clients and projects?
Hi Guys, like many of you I work with AI agents every day and across several clients. The major time cost for me is to re-explaining context and past decisions, client rules, that we already do. To finish every tools as its own memory.So I’m curious how you actually handle this:1. When you start a new session, how do you give context to the agent (context came from chat, mail, ticket ...) ?2. Do you use more than one agent type (claude, codex ...) ? And if yes, how do you carry context between agents ?3. If you work for several clients, how do you keep their context separate ?4. The last time something went wrong ( an agent used a rejected approach, forgot a decision ...), what happened, and how much time did it cost to you ?5. Did you paid or built a tool that helped you with these problems ? Do you still use it ?I’m exploring whether to build something, so I’m trying to learn how other people really work. Thanks for your return.
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
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A tool to manage and transfer AI agent context efficiently across multiple clients and projects.
The pain
The gap
Build angle
Strengths
- Clear and common pain point among AI users
- Growing market of AI agent users across industries
- Moderate technical complexity suitable for a small team
Risks
- Uncertain willingness to pay for such a tool
- Potential competition from large AI platform providers
- Challenges in integrating diverse agent types and client data securely
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 70
- Willingness to pay
- 65
- Buildability
- 75
The problem of managing AI agent context across clients and projects is clear and painful, there is a gap in existing tools, some willingness to pay exists, and the solution is moderately buildable by a solo founder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a real pain with a sizable market but lacks clear evidence of strong demand or a unique solution that commands high value.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The concept targets a niche but growing user base with a tangible problem, though monetization and defensibility remain uncertain.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The problem is well defined and relevant, the solution is feasible, but customer acquisition and retention strategies need more clarity.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The idea solves a common pain for AI practitioners, has potential for early adopters, but requires validation on willingness to pay and product-market fit.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
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Postgres extension for BM25 relevance-ranked full-text search
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AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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