Delivery coordination
Track tasks, read project updates, identify blockers, draft client-ready summaries, and keep delivery moving without manual status archaeology.
commercial AI agentsoperators, not chatbots
AI agent operators for real business work
Tessera designs and runs AI agent operators that take ownership of bounded workflows: checking inputs, making decisions, preparing outputs, escalating exceptions, and keeping a visible audit trail.
This is for teams that want leverage without pretending a chatbot is an employee. The goal is clear: useful work completed safely, repeatedly, and tied to revenue or delivery throughput.
Search intent
Built for buyers comparing agentic operators, not casual AI curiosity.
You are not looking for another AI demo. You are looking for an operator model: what can be delegated, where humans stay in control, what tools the agent may touch, and how performance is measured.
Tessera starts with one high-value workflow, builds the operating boundary, deploys the agent, and keeps the system observable so it can earn trust before it expands.
Use cases
Research and synthesis
Turn messy source material into structured decisions, reports, competitive intelligence, and implementation briefs your team can act on.
Revenue operations
Monitor leads, follow-ups, quotes, invoices, and client opportunities so commercial work does not disappear under busywork.
Technical operations
Watch logs, triage alerts, create implementation tasks, and hand off scoped fixes to developers or coding agents with useful context.
Content operations
Convert product knowledge, case studies, and research into drafts, internal links, metadata, and distribution assets.
Executive assistance
Maintain context across email, calendar, notes, tasks, and decisions without forcing leaders to become prompt engineers.
Proof
Commerce execution
Crate Clothing moved from a slow storefront to a faster Hydrogen architecture, creating practical proof for ecommerce systems work.
Operator workflows
Tessera uses agentic systems internally for research, reporting, task routing, implementation handoffs, and delivery monitoring.
Measurable improvements
Recent work reduced LCP from 31.9s to 2.2s and lifted Lighthouse performance from 23 to 93.
Process
01
Map
Identify the workflow, decision points, systems touched, permissions required, and failure modes before any agent is built.
02
Prototype
Ship the thinnest useful operator: scoped tools, visible logs, human approval gates, and a narrow success metric.
03
Operate
Run it against real work, tune the prompts and boundaries, then document the operating rhythm your team can trust.
04
Compound
Expand from one proven workflow into a small fleet of agents that share context without creating a black box.
FAQ
What makes this different from a chatbot?
A chatbot waits for prompts. An operator has a defined workflow, allowed tools, success criteria, escalation rules, and a log of what it did.
Do agents act without approval?
Only inside agreed low-risk boundaries. Anything external, destructive, financial, or reputation-sensitive keeps a human approval gate.
Where should we start?
Start where work is frequent, rule-shaped, commercially meaningful, and currently handled by a senior person who should be doing higher-value work.
Can this work with our existing tools?
Yes. The point is to operate across the tools you already use — email, Slack, Asana, Shopify, GitHub, analytics, docs — not add another dashboard nobody opens.
If the workflow matters, make it operational.
Bring one messy workflow. Tessera will map the risk, define the operator boundary, and show the smallest useful system worth deploying.