No-Code AI Trading Agent Workspace with Prompt-Based Chains and Live Observability
Feature · Agents
Agent Workspace
Design, monitor, and tune no-code AI trading agents. Prompt-based chains, modality controls, RL-calibrated risk, and a 60-second live observability pulse — all in one place.
[Hero screenshot placeholder — drop a 1920×1080 Agent Workspace hero shot here]
What the Agent Workspace is, in plain language
The Agent Workspace is where you build, watch, and improve your own AI (artificial intelligence — software that can reason and make decisions) trading helpers. In Treeova, those helpers are called agents. An agent is not a single chatbot message. It is a small program that runs a chain of tools — for example, pull a quote, check the options chain, score the setup, write you a note — in a specific order, and finishes when its job is done.
You describe what you want the agent to do in everyday language. Treeova's Chain Generator reads your description and builds the work for you as a directed acyclic graph (DAG — a flow diagram where each step feeds the next and the work never loops back on itself). You do not write any code. If you want more control later, you can open the chain and edit individual steps, pin a specific tool, or change the prompt that guides the agent's reasoning.
Every agent is locked into one of two modes, called a modality. An Alert-only agent can watch the market and send you a notification (email, Discord, or a webhook — a small message your own software can receive), but it cannot place an order. A Trading agent is allowed to place orders, but only against a paper or live broker account you have explicitly assigned to it. The rule is enforced at the place where tools actually run, so a Trading tool will not fire inside an Alert-only agent even if the chain accidentally lists it.
Every Trading agent also carries a risk envelope — a fixed set of safety limits that travel with the agent. These include a stop-loss (the loss level at which the agent must close the trade), a profit target (the gain level at which it takes profit), a 20% concentration cap (no single position may exceed 20% of the account), and a $50 minimum buying-power (BP — the cash and margin available to open a new trade) gate. Treeova's reinforcement learning system — called RL (reinforcement learning — a kind of machine learning that improves a policy by watching the results of its past decisions) — studies how each setting actually performed on closed trades and then suggests small adjustments to those knobs. You can choose to review each suggestion, ignore them, or let safe ones apply automatically.
While an agent runs, the Workspace emits a heartbeat called the observability pulse every 60 seconds. The pulse shows how far along the chain is, how long each tool took, which AI model handled each step, why the agent declined any action it skipped, and what the RL system recorded from the run. Pulses stream live and are kept for 24 hours so you can replay a full session step by step. Together with the per-agent memory system (which consolidates older notes and decays cleanly when you purge it), this means you can ship a new agent in minutes and still audit every decision it ever made.
How it works
1
Describe the strategy
E.g. 'scan for high-IV-rank SPX put credit spreads and alert me when delta-30 setups appear'.
2
Pick a modality
Alert-only notifies you. Trading executes against a delegated paper or live broker account with a pre-attached risk envelope.
3
Monitor and tune
Watch the pulse panel, review chain decisions, and let RL calibration sharpen risk knobs. Adjust prompts anytime.
What's inside
Prompt-Based Chains
Describe a strategy in plain language; the Chain Generator builds the tool DAG and you tune from there.
Modality Controls
Alert-only vs Trading agents are enforced at the executor — a blocked tool cannot fire even if the chain references it.
RL-Calibrated Risk
Stop-loss, profit target, and position concentration knobs sharpen over time via RL Calibration Math v2.
Live Observability Pulse
60-second pulses on chain progress, tool latencies, decline reasons, and RL writes — replayable for 24 hours.
Per-Agent Delegation
Bind each agent to a specific paper or live broker account. Switch delegations without rebuilding the strategy.
Agent Memory Lifecycle
Per-agent memory consolidates and decays cleanly — purges remove rows from both working and consolidated stores.
See it in action
[Screenshot: chain DAG editor]
Inspect and edit the generated chain node-by-node.
[Screenshot: pulse panel]
60-second observability pulses with full replay.
Use cases
The Agent Workspace meets traders where they already work — whether that is scanning for setups before the open, automating a single rules-based strategy, or running a small fleet of specialised agents that each own one job.
The discretionary trader who wants a second pair of eyes
You trade discretionarily but want an agent to watch the market while you are away from the screen. Spin up an Alert-only agent with a prompt like 'notify me when SPY 0DTE put-side IV rank crosses 60 and dark-pool sentiment turns bullish.' The agent runs every five minutes during US hours and pushes Discord alerts.
Outcome: Zero execution risk (Alert-only modality blocks every order tool at the executor), full audit replay in the Pulse panel, and you decide whether to act on each alert.
The rules-based options seller who wants automation
You sell delta-30 put credit spreads on /ES every Monday. Build a Trading agent delegated to your paper account first: prompt the chain to scan for IVR above 40, build the spread, size against a 1% portfolio risk envelope, set a 50% profit target and 2× credit stop. Watch it run for two weeks, accept the RL calibration suggestions, then re-delegate to a live broker account.
Outcome: The same agent now runs Monday after Monday with stop-loss, profit target, and concentration caps enforced — and every fill writes back into RL Calibration Math v2 so sizing sharpens over time.
The systematic builder running a small fleet
You want one agent per regime: a trend-day breakout agent on QQQ, a mean-reversion agent on /CL, and an earnings-IV-crush agent on SPY components. Each gets its own prompt, its own delegated paper account, and its own modality. The Pulse panel streams 60-second beats for all three side by side; modality enforcement guarantees the breakout agent cannot fire an order in the IV-crush agent's account.
Outcome: Specialised agents that compete for capital under your governance — no shared state surprises, no cross-account leakage, and full RL feedback loops per strategy.
Frequently asked
What is the Agent Workspace?
Where you design, deploy, and monitor no-code AI trading agents. Each agent runs a prompt-generated chain across 50+ market intel, options analytics, and execution tools, with modality separating alert agents from execution agents.
Do I need to write any code?
No. Plain-language prompts generate the tool DAG. Power users can edit chains node-by-node, attach custom prompts, or pin specific tools — but programming is never required.
What's the difference between Alert and Trading modality?
Alert-only agents notify via email/Discord/webhook and cannot place orders. Trading agents execute against a delegated broker account. Modality is enforced at the executor — blocked tools cannot fire.
How are agent risk parameters set?
Each agent carries an RL-calibrated envelope: stop-loss, profit target, 20% concentration cap, and a $50 minimum BP gate. RL math v2 proposes adjustments from settled outcomes; you decide accept/reject/auto-apply.
Can I see what an agent is doing in real time?
Yes — a 60-second observability pulse streams chain progress, tool latencies, model legs, decline reasons, and RL writes into the Pulse panel. Pulses are realtime-subscribed and retained 24 hours for replay.
Feature · AgentsAgent WorkspaceDesign, monitor, and tune no-code AI trading agents. Prompt-based chains, modality controls, RL-calibrated risk, and a 60-second live observability pulse — all in one place.[Hero screenshot placeholder — drop a 1920×1080 Agent Workspace hero shot here]What the Agent Workspace is, in plain languageThe Agent Workspace is where you build, watch, and improve your own AI (artificial intelligence — software that can reason and make decisions) trading helpers. In Treeova, those helpers are called agents. An agent is not a single chatbot message. It is a small program that runs a chain of tools — for example, pull a quote, check the options chain, score the setup, write you a note — in a specific order, and finishes when its job is done.You describe what you want the agent to do in everyday language. Treeova's Chain Generator reads your description and builds the work for you as a directed acyclic graph (DAG — a flow diagram where each step feeds the next and the work never loops back on itself). You do not write any code. If you want more control later, you can open the chain and edit individual steps, pin a specific tool, or change the prompt that guides the agent's reasoning.Every agent is locked into one of two modes, called a modality. An Alert-only agent can watch the market and send you a notification (email, Discord, or a webhook — a small message your own software can receive), but it cannot place an order. A Trading agent is allowed to place orders, but only against a paper or live broker account you have explicitly assigned to it. The rule is enforced at the place where tools actually run, so a Trading tool will not fire inside an Alert-only agent even if the chain accidentally lists it.Every Trading agent also carries a risk envelope — a fixed set of safety limits that travel with the agent. These include a stop-loss (the loss level at which the agent must close the trade), a profit target (the gain level at which it takes profit), a 20% concentration cap (no single position may exceed 20% of the account), and a $50 minimum buying-power (BP — the cash and margin available to open a new trade) gate. Treeova's reinforcement learning system — called RL (reinforcement learning — a kind of machine learning that improves a policy by watching the results of its past decisions) — studies how each setting actually performed on closed trades and then suggests small adjustments to those knobs. You can choose to review each suggestion, ignore them, or let safe ones apply automatically.While an agent runs, the Workspace emits a heartbeat called the observability pulse every 60 seconds. The pulse shows how far along the chain is, how long each tool took, which AI model handled each step, why the agent declined any action it skipped, and what the RL system recorded from the run. Pulses stream live and are kept for 24 hours so you can replay a full session step by step. Together with the per-agent memory system (which consolidates older notes and decays cleanly when you purge it), this means you can ship a new agent in minutes and still audit every decision it ever made.How it works1Describe the strategyE.g. 'scan for high-IV-rank SPX put credit spreads and alert me when delta-30 setups appear'.2Pick a modalityAlert-only notifies you. Trading executes against a delegated paper or live broker account with a pre-attached risk envelope.3Monitor and tuneWatch the pulse panel, review chain decisions, and let RL calibration sharpen risk knobs. Adjust prompts anytime.What's insidePrompt-Based ChainsDescribe a strategy in plain language; the Chain Generator builds the tool DAG and you tune from there.Modality ControlsAlert-only vs Trading agents are enforced at the executor — a blocked tool cannot fire even if the chain references it.RL-Calibrated RiskStop-loss, profit target, and position concentration knobs sharpen over time via RL Calibration Math v2.Live Observability Pulse60-second pulses on chain progress, tool latencies, decline reasons, and RL writes — replayable for 24 hours.Per-Agent DelegationBind each agent to a specific paper or live broker account. Switch delegations without rebuilding the strategy.Agent Memory LifecyclePer-agent memory consolidates and decays cleanly — purges remove rows from both working and consolidated stores.See it in action[Screenshot: chain DAG editor]Inspect and edit the generated chain node-by-node.[Screenshot: pulse panel]60-second observability pulses with full replay.Use casesThe Agent Workspace meets traders where they already work — whether that is scanning for setups before the open, automating a single rules-based strategy, or running a small fleet of specialised agents that each own one job.The discretionary trader who wants a second pair of eyesYou trade discretionarily but want an agent to watch the market while you are away from the screen. Spin up an Alert-only agent with a prompt like 'notify me when SPY 0DTE put-side IV rank crosses 60 and dark-pool sentiment turns bullish.' The agent runs every five minutes during US hours and pushes Discord alerts.Outcome: Zero execution risk (Alert-only modality blocks every order tool at the executor), full audit replay in the Pulse panel, and you decide whether to act on each alert.The rules-based options seller who wants automationYou sell delta-30 put credit spreads on /ES every Monday. Build a Trading agent delegated to your paper account first: prompt the chain to scan for IVR above 40, build the spread, size against a 1% portfolio risk envelope, set a 50% profit target and 2× credit stop. Watch it run for two weeks, accept the RL calibration suggestions, then re-delegate to a live broker account.Outcome: The same agent now runs Monday after Monday with stop-loss, profit target, and concentration caps enforced — and every fill writes back into RL Calibration Math v2 so sizing sharpens over time.The systematic builder running a small fleetYou want one agent per regime: a trend-day breakout agent on QQQ, a mean-reversion agent on /CL, and an earnings-IV-crush agent on SPY components. Each gets its own prompt, its own delegated paper account, and its own modality. The Pulse panel streams 60-second beats for all three side by side; modality enforcement guarantees the breakout agent cannot fire an order in the IV-crush agent's account.Outcome: Specialised agents that compete for capital under your governance — no shared state surprises, no cross-account leakage, and full RL feedback loops per strategy.Frequently askedWhat is the Agent Workspace?Where you design, deploy, and monitor no-code AI trading agents. Each agent runs a prompt-generated chain across 50+ market intel, options analytics, and execution tools, with modality separating alert agents from execution agents.Do I need to write any code?No. Plain-language prompts generate the tool DAG. Power users can edit chains node-by-node, attach custom prompts, or pin specific tools — but programming is never required.What's the difference between Alert and Trading modality?Alert-only agents notify via email/Discord/webhook and cannot place orders. Trading agents execute against a delegated broker account. Modality is enforced at the executor — blocked tools cannot fire.How are agent risk parameters set?Each agent carries an RL-calibrated envelope: stop-loss, profit target, 20% concentration cap, and a $50 minimum BP gate. RL math v2 proposes adjustments from settled outcomes; you decide accept/reject/auto-apply.Can I see what an agent is doing in real time?Yes — a 60-second observability pulse streams chain progress, tool latencies, model legs, decline reasons, and RL writes into the Pulse panel. Pulses are realtime-subscribed and retained 24 hours for replay.Continue exploringMetaChart Trade Cockpit AI Trading Agents Prompt-Based Agents Meta Agent Stack Whitepaper ASI Evolution Whitepaper Build your first agent