Task-Agent Mode — Deep Dive
TaskFlow IDE: Intelligent Multi-Agent Orchestration
DAEDALUS TaskFlow IDE redefines how complex work gets done. From a single natural-language prompt,
it intelligently decomposes tasks, assembles specialized agent teams, executes real-time flow graphs,
audits quality autonomously, and heals itself when things go wrong — all without human micromanagement.
Below is a detailed walkthrough of every major capability.
LLM Smart Decomposition
Smart Flow Creation
Real-Time Node Execution
Autonomous Agent Assignment
Real Auditor Check
Self-Learning
Self-Healing
Dynamic Orchestration
Memory Selection
Function Tool Calls
Artifact Management
Flow-Aware AI Chat
12+
Specialized Agent Types
0
Manual Interventions Needed
Real-Time
Node Execution Visibility
The Dashboard is designed for zero cognitive overhead. At a glance you see which flows are running,
which agents are busy, which tasks are blocked, and where the auditor has raised flags.
The sidebar surfaces quick actions: create a task, spawn an agent, inspect an artifact, or trigger a healing cycle.
- Live flow status grid (running / paused / failed / completed)
- Per-agent activity timeline
- System health indicators (CPU, memory, LLM token usage)
- One-click task creation & agent assignment
- Rewarding & feedback loop controls
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Main Dashboard
Full command center — live flows, agent states, metrics, and quick-action controls in one unified view.
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Agent Communication & Rewarding
Agents exchange messages, negotiate task ownership, and receive performance rewards — all surfaced in real time.
How it works: The Decomposition Engine runs a multi-pass reasoning chain (ReAct + Tree-of-Thought).
Pass 1 extracts intentions. Pass 2 identifies dependencies. Pass 3 sizes each sub-task and scores parallelism.
Pass 4 generates the flow graph. The entire process typically completes in under 3 seconds,
producing a production-ready execution plan with no human input.
- Natural language → structured task tree in <3 s
- Multi-pass ReAct + Tree-of-Thought reasoning
- Automatic dependency graph construction
- Parallelism scoring & effort estimation
- Editable task tree before execution
- Context-aware: reads your project files & history
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Smart Task Creation Panel
Enter a goal, choose scope, and watch the LLM break it into an actionable, dependency-aware task graph ready for execution.
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Decomposition Output → Flow Node
Each decomposed sub-task becomes a live node in the flow graph, wired with correct dependency edges.
What makes it different: Unlike static Gantt charts or simple to-do lists, the TaskFlow graph
is a living execution engine. It routes work around failures (Watchdog integration),
auto-reschedules blocked nodes when dependencies resolve, and surfaces bottlenecks visually.
You can interact with any running node — pause it, reassign it, inject additional context, or skip it entirely.
- Interactive DAG (Directed Acyclic Graph) visualization
- 3 edge types: Strict Sequential · Parallel · Soft Dependency
- Live node state: pending / running / completed / failed / blocked
- Click any node → inspect agent logs, artifacts, audit score
- Watchdog integration: auto-reroute on failure
- Multi-flow management (run dozens in parallel)
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Flow Running — Live State
Nodes turn green as completed, amber while running, red if blocked.
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Parallel Execution Paths
Multiple agent branches executing simultaneously — true parallel orchestration.
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Dependency Edge Types
Visual distinction between strict, parallel, and soft dependency relationships.
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Execution Timeline View
Phase-based timeline: Foundation → Implementation → Finalization with milestone markers.
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Node Detail Drawer
Click any node to reveal its agent, sub-tasks, logs, artifacts, and auditor verdict.
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Agent Execution Inside Node
Live agent reasoning log — see exactly what the agent is thinking, deciding, and doing.
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Failure Recovery
Watchdog detects failure; Daedalus auto-reroutes the blocked node.
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Multi-Flow Overview
Multiple independent flows managed simultaneously from one dashboard.
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Completed Flow Report
Post-execution summary: artifacts produced, time taken, quality scores.
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Advanced Flow Graph
Complex real-world flow with 15+ nodes, multiple agent types, and hybrid dependencies.
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Sub-Flow Nesting
Flows can spawn child flows — enabling recursive task delegation to specialized teams.
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Flow Metrics Panel
Per-flow performance metrics: throughput, latency per node, agent efficiency scores.
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Flow Audit Trail
Every decision logged: when, by whom, why — full immutable audit trail for compliance.
The Agent Assignment Engine runs a multi-criteria decision process:
capability matching (does this agent know how to do this?), availability check (is the agent's queue clear?),
performance history (how did it handle similar tasks before?), and cost optimization (which assignment minimizes total flow time?).
If no existing agent fits, the system spawns a new specialized agent on the fly using the Agent Genesis Engine.
- Multi-criteria agent-task matching
- Real-time workload balancing across agent pool
- Dynamic agent spawning when no match exists
- Performance-weighted assignment scoring
- Manual override always available
- Reward signals feed back into future assignments
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Agent Task Assignment Panel
Visual matrix showing which agent is assigned to which task — with capability score, workload, and match confidence.
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Agent Profile & Capability Info
Deep dive into any agent: roles, skills, cognitive profile, active tasks, and performance history.
The Real Auditor runs asynchronously in parallel with agent execution. It evaluates:
code correctness, security vulnerabilities, performance patterns, architecture adherence, documentation completeness,
and test coverage. Each dimension produces a sub-score, combined into a single Audit Score out of 100.
Outputs scoring below the threshold are automatically sent back for revision — closing the quality loop without human intervention.
- 6-dimension quality scoring (correctness, security, perf, arch, docs, tests)
- Audit score 0–100 per task output
- Auto-rejection & revision loop for low scores
- Line-by-line finding annotations
- Configurable thresholds (organization policy)
- Audit history log for compliance & traceability
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Audit Score Dashboard
Per-artifact audit score with sub-dimension breakdown — see exactly where quality gaps exist and what the agent needs to fix.
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Auditor Node in Flow Graph
The Auditor manifests as a dedicated node in the flow — outputs only pass through once quality gates are cleared.
Self-Learning: After each flow completes, a learning signal is extracted: what worked,
what was slow, what produced low audit scores. This signal updates the agent's knowledge substrate
and influences future task planning and agent assignment decisions.
Self-Healing: When a node fails, the Watchdog classifies the failure type
(tool timeout, LLM hallucination, dependency deadlock, resource exhaustion) and applies the
appropriate recovery strategy: retry, reassign, rollback, or spawn a specialist healing agent.
- Per-flow learning signals extracted automatically
- Agent knowledge substrate updated post-execution
- 5 failure classification types with dedicated recovery strategies
- Watchdog monitors all nodes 24/7 with <1 s response time
- Healing history log — track every recovery event
- Integrates with Agent University for deep retraining when needed
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Learning from Audit Feedback
Audit findings feed directly into the agent's learning pipeline as structured training signals.
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Self-Healing Agent Profile
View an agent's healing history — number of recoveries, types of failures overcome, and improvement trajectories.
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Rewarding System
Positive reinforcement signals reinforce successful patterns — agents improve continuously with every task cycle.
The Agent Dialog system implements the ACL (Agent Communication Language) protocol.
Agents can: request help from peers, broadcast availability, form ephemeral sub-teams,
escalate to the orchestrator, and report task outcomes with structured payloads.
All communication is observable and replayable — critical for debugging complex multi-agent workflows.
- ACL-based inter-agent messaging
- Task negotiation & delegation protocols
- Escalation chains to orchestrator
- Full conversation replay & inspection
- Pheromone-based stigmergic coordination for large teams
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Agent Dialog Panel
Complete inter-agent conversation thread — see how agents reason together, delegate work, and reach consensus on complex tasks.
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Communication & Dynamics Board
Real-time visualization of agent communication frequency, collaboration patterns, and reward distribution across the team.
The Chat Panel operates in Flow Mode — it's not a generic chatbot. It has direct access
to the live execution graph, agent states, task outputs, and audit results. You can say
"pause the DB migration node", "summarize what Agent-3 has produced so far",
or "why is the authentication task blocked?" — and get instant, accurate, context-grounded answers.
- Live flow context injection (knows your current flow state)
- Natural language flow control (pause / resume / skip)
- Artifact inspection & code explanation on demand
- Mid-flight task modification without breaking dependencies
- Streaming responses with syntax-highlighted code blocks
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Chat Panel in Flow Mode
Context-aware chat docked alongside the live flow graph — query, control, and guide your flow through natural conversation.
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Task Clarification Dialog
The AI proactively asks clarifying questions when task requirements are ambiguous — reducing rework.
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Code Explanation & Review
Ask the AI to explain any artifact in the flow — it reads the code and explains it in full context.
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Flow Status Query
Natural language status queries: "What's left to do?" returns a structured live summary instantly.
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Mid-Flight Modification
Inject new requirements mid-execution — the AI propagates changes through the dependency graph safely.
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Memory & Context Selection
Choose which memory layers the AI uses: project KB, conversation history, artifact context, or external docs.
Every tool call is transparent and inspectable. The Function Tool Call panel shows:
the function name, input parameters, execution duration, return value, and whether the result was
used successfully. Tool calls are rate-limited, sandboxed, and logged — giving you confidence
that agents are doing exactly what they're supposed to do.
- Full tool registry: file, terminal, API, DB, browser, git, test runner
- Transparent call log with input/output inspection
- Sandboxed execution — agents cannot exceed granted permissions
- Tool retry logic with exponential backoff
- MCP (Model Context Protocol) server integrations
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Tool Call Log Panel
Every function invocation by every agent — tool name, params, duration, result status — in a scannable real-time feed.
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Full-Screen Tool Inspection
Expand any tool call to inspect the complete JSON payload, response body, and agent's interpretation of the result.
The Artifact System is the production output layer of TaskFlow.
Artifacts can be: viewed inline (with syntax highlighting), downloaded, compared (diff view),
promoted to the project repository, or rejected (triggering an agent revision cycle).
Every artifact carries its audit score, creator agent ID, creation timestamp, and revision history.
- All agent outputs captured as versioned artifacts
- Inline viewer with syntax highlighting for 30+ languages
- Diff view between artifact versions
- One-click promotion to project repository
- Audit score & quality badge on every artifact
- Reject → revision loop with targeted agent feedback
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Artifact Establishment
The moment an agent produces output, it's immediately registered as an artifact — linked to its parent task node, versioned, and queued for audit.
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Artifact Gallery & Management
Browse all artifacts from a flow in one gallery view. Filter by type, agent, audit score, or status. Promote, compare, or reject in one click.
Ready to See It Live?
TaskFlow IDE is available now in DAEDALUS
Request a live demo or trial access to experience every feature shown above in your own environment —
with your own tasks and your own agents.
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