Closed-loop software execution

Every project,
planned smarter.

Optivia breaks your project into a task graph, scopes context per task, builds a specialized agent fleet, and compounds verified learning across every engineer on the team.

The ingestion worker leaks 8GB of memory over 6 hours. Find the leak.
Without$0.42
+ Optivia$0.11
−74%
1Bash"ps aux | grep worker…"
2Bash"py-spy dump --pid $…"
3Queue buildup? Check the asyncio queue.
4Bash"grep -r asyncio.Queue …"
1Bash"ps aux | grep worker…"
2▒ CONTEXTscoped worker.py + queue.py
3▒ ROUTESonnet 4.6 · unbounded buffer

Drops into your stack

LangChainLangGraphOpenAI Agents SDKClaude Agent SDKAnthropic SDKGitHubLinearJira
0
% less agent spend
0
capabilities
0
runtime
recursive learning

How it works

How a project moves through it

Every goal walks the same rail — intake to verified learning — and every verified result loops back into the task graph, so the system gets sharper with each pass.

The closed loop

Task graph, scoped context, agent fleets, and verified results form one self-reinforcing cycle. Nothing is thrown away — each verified outcome re-enters the graph as context for the next run.

Capabilities

Six capabilities.
One runtime.

Each one targets a specific failure mode in autonomous work. None require changes to your agent code.

01 / 06
01Task decomposition
Dependency graph12 nodes
T1scaffold schemaDONE
T2migrate auth → graph→ T1
T3rate-limit middleware→ T1
T4integration tests→ T2,T3

The terminal app

Watch the fleet work.

Optivia ships as a terminal app. Press play for the full run — approve the task graph, launch the fleet, and watch every verified result land. Jump to any screen below.

optivia · home · demo0:00 / 0:20

Home — Plan the work · build the fleet · ship it.

Return on investment

Do the math.

Drag the sliders. Scoping context and routing each task to the cheapest capable model typically removes the majority of agent spend.

2,016 agent tasks / month · 21 working days · routing & scoping applied per task.

Without Optivia$847 /mo
+ Optivia$237 /mo
Saved / month
$610
−72%

Integrations

Drops into the agent framework
you already use.

from langchain.agents import create_agent
from optivia import Optivia
 
opt = Optivia(api_key="opt_live_...")
plan = opt.plan(project="acme/api", task="Refactor the auth module")
 
agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=plan.tools,
    middleware=[opt.middleware(plan)],
)
 
# each node runs scoped, routed, and verified
for node in plan.graph:
    agent.invoke({"messages": [("user", node.brief)]})

Other framework? Drop us a line.

Get started

Plan your next project smarter.

Turn a goal into a verified task graph, a scoped agent fleet, and learning that compounds with every run.