Executive-level control
over AI agents
The management layer for autonomous AI. Delegate tasks, control memory and context, govern tool access reliably execute complex workflows at scale.
The Problem
Current AI agents weren't built for real work
Most agent frameworks optimize for demos, not production. They break down when you need consistent, auditable execution across complex workflows.
Context bloat
Agents lose track of what matters. Unbounded context windows lead to hallucinations, missed details, and degraded performance over long sessions.
Lack of control
No visibility into agent decisions. No ability to constrain behavior, limit tool access, or enforce execution boundaries when things go wrong.
Poor reliability
Single-agent systems fail silently. No retry logic, no task handoff, no way to recover from partial failures in multi-step workflows.
The Solution
An executive layer for AI infrastructure
Executive AI sits above your specialized agents. It understands goals, decomposes tasks, assigns work, and maintains oversight like a technical lead for your AI workforce.
Executive AI Agent
Goal understanding • Task decomposition • Agent coordination • Quality assurance
Code Agent
Writes, reviews, and refactors code
Research Agent
Gathers and synthesizes information
Data Agent
Queries, transforms, and analyzes data
Ops Agent
Executes operational workflows
Governance Layer
Memory Control
Scoped, persistent memory per agent
Context Management
Explicit context passing and pruning
Tool Governance
Dynamic permissions and rate limits
How It Works
Five-stage execution pipeline
Every workflow follows a predictable, observable pattern from goal to completion.
Understand goal
Executive AI receives a high-level objective and analyzes requirements, constraints, and success criteria.
Decompose tasks
The goal is broken into discrete, assignable tasks with clear dependencies and execution order.
Assign agents
Each task is routed to the most capable specialized agent based on requirements and current load.
Control memory & tools
Agents receive scoped context and explicit tool permissions. No more than what's needed for the task.
Evaluate & iterate
Results are validated against success criteria. Failed tasks retry with adjusted parameters or escalate.
Capabilities
Infrastructure-grade AI operations
The primitives you need to run AI agents in production environments with confidence.
Hierarchical agent management
Define agent hierarchies with clear delegation paths. Executives supervise specialists, specialists can spawn sub-agents for complex subtasks.
Explicit memory control
Agents operate with scoped, persistent memory. Control what each agent remembers, forgets, and can access across sessions.
Dynamic tool permissioning
Grant and revoke tool access at runtime. Enforce rate limits, audit usage, and sandbox dangerous operations.
Long-horizon task reliability
Built for workflows that span hours or days. Checkpoint progress, handle interruptions, and resume without data loss.
Full execution trace & observability
Every decision, tool call, and agent interaction is logged. Debug failures, audit behavior, and optimize performance.
Structured output validation
Define expected output schemas. Agents are constrained to produce valid, parseable results that integrate cleanly with your systems.
Use Cases
Built for your most complex workflows
Executive AI handles the coordination layer so your teams can focus on high-value decisions.
Software engineering workflows
From feature requests to deployed code. Executive AI coordinates planning, implementation, testing, and deployment agents across your entire development pipeline.
Example workflows
- Automated PR review and feedback incorporation
- Cross-repo refactoring with dependency analysis
- Bug triage, reproduction, and fix generation
- Documentation generation and maintenance
Research & analysis
Deep research across large information spaces. Specialized agents handle data gathering, synthesis, and structured output generation with full source attribution.
Example workflows
- Competitive intelligence and market analysis
- Technical due diligence on vendors and tools
- Literature reviews with citation management
- Data extraction from unstructured sources
Internal operations automation
Reliable execution of repetitive operational tasks. Define once, run continuously with monitoring, alerting, and human-in-the-loop escalation.
Example workflows
- Onboarding workflow automation
- Vendor management and procurement
- Compliance monitoring and reporting
- Cross-system data synchronization
Why It's Different
A management layer, not just orchestration
Most agent frameworks chain prompts together. Executive AI provides the governance, control, and reliability infrastructure that production workloads require.
Ready to bring executive control to your AI infrastructure?
We're onboarding engineering and operations teams who need reliable, auditable AI agent execution. Request access to join the private beta.