A retrieval system that surfaces relevant information is useful. An agentic system that surfaces relevant information and then does something with it is transformative. The difference is the gap between a reference librarian and a chief of staff.
Tessera is not a search engine for my life. It is an agent that operates within the context of my life. That distinction shapes every architectural decision from this point forward.
What Agentic Means Here
In the AI community, “agentic” has become a buzzword that means everything and nothing. For Tessera, I define it precisely: an agentic system can decompose a complex request into subtasks, execute those subtasks using available tools and context, maintain state across the execution, and produce a coherent result that addresses the original request.
When I say “prepare me for the remediation call with the client whose Exchange migration failed,” Tessera should not just find documents about Exchange migrations. It should identify the specific client, pull the migration timeline, find the failure point from ticket history, retrieve my notes on the root cause analysis, locate the escalation communications, find precedent from similar failures, and synthesize a briefing that includes what happened, what I decided, what the client expects, and what my options are.
That is six or seven retrieval operations, each informed by the results of the previous ones, assembled into a structured output. No single query does this. An agent does this.
The Planning Layer
The agentic architecture has three components: a planner, an executor, and a verifier. The planner takes the user request and decomposes it into a retrieval plan, a sequence of queries and operations that will produce the information needed. The executor runs the plan against the trimodal retrieval system. The verifier checks the results for completeness, coherence, and accuracy before presenting them.
The planner is where the intelligence lives. It needs to understand what information is required, what order to retrieve it in (because later queries depend on earlier results), and when to stop. A bad planner either under-retrieves, missing critical context, or over-retrieves, burying the answer in noise.
I am building the planner as a chain-of-thought system that explicitly reasons about what it needs before it starts looking. The reasoning trace is visible to me, which means I can audit why Tessera retrieved what it retrieved. Transparency is not optional in an agentic system that operates on your personal data.
Technical Remediation as the First Use Case
I chose technical remediation as the first agentic use case because it is the most demanding. A remediation scenario involves multiple systems, multiple stakeholders, time pressure, and a decision history that spans months or years. If the agentic layer works for remediation, it will work for everything else.
The typical remediation workflow: something breaks, the client escalates, I need context fast. What is this client’s environment? What have we done for them before? What is the service agreement? Who on my team knows this stack? What is the vendor’s escalation path? What did I do last time something similar happened?
Today, I answer those questions from memory, email searches, and ticket system queries. It takes twenty to forty minutes to build context before I can make a decision. Tessera should compress that to under five minutes by running the retrieval plan in parallel and presenting a synthesized briefing.
Five minutes instead of forty. That is not a marginal improvement. That is a structural change in how I operate under pressure.