Agentic Engineering Framework
Ship with AI agents. Keep engineering in command.
DinoStack is an agentic engineering framework by Space Dinosaurs. It gives AI coding agents a disciplined operating model for planning, delegation, review, QA, context management, and long-running project memory.
DinoStack adds the missing multi-agent engineering harness: structured workflows, specialist agents, adversarial review, risk-aware routing, and repeatable delivery patterns.
The DinoStack Attention Architecture
Humans supply intent and judgment. The conductor classifies risk, keeps context light, fans work out to specialists, and only lets reviewed output move forward. Memory flows back into the next session, so the system compounds instead of restarting cold.
The Problem & Our Origin
Coding agents can move fast.
They can also drift, overfill context, skip review, forget project conventions, and start implementing before the work is properly framed. DinoStack turns agentic coding into an engineering system.
It helps your agent decide when to act directly, when to plan, when to spawn specialist agents, when to review, when to verify in-browser, and when to preserve decisions for the next session.
Small tasks stay lightweight. Risky work gets structure. Complex work gets orchestration.
Outcomes, velocity, and operational quality
Space Dinosaurs builds AI-augmented systems for teams that care about outcomes, velocity, and operational quality.
DinoStack is our open agentic engineering framework: the protocol we use to make AI-assisted development more reliable, more inspectable, and more repeatable. It brings field-tested patterns into the open so developers and teams can adapt, extend, and improve them.
What DinoStack does
Plans before it edits
DinoStack helps agents frame the work before they touch the code. For meaningful changes, it defines the goal, constraints, files, risks, success criteria, and verification path.
That means fewer half-built branches, fewer invented requirements, and fewer “almost done” implementations that collapse during review.
Routes work to the right agent
DinoStack treats the main agent like a conductor, not a dumping ground.
Specialist agents handle focused jobs: architecture, implementation, debugging, adversarial review, browser QA, security review, performance analysis, release coordination, documentation, and more.
Each agent gets a narrow brief and clean context. The main thread stays sharp and available for input.
Reviews with teeth
DinoStack includes an adversarial review loop designed to catch flawed assumptions, missing edge cases, and implementation drift.
The reviewer starts fresh. It sees the output and the brief, not the implementer's reasoning trail. That independence makes the review harder to charm and easier to trust.
Critical findings block. Major findings require action or an explicit decision.
Verifies behavior, not vibes
When work changes visible UI or behavioral output, DinoStack can route it through runtime QA.
The QA agent opens the app, navigates, clicks, screenshots, checks browser errors, and reports what actually happened.
“Looks right in code” is not the same as “works for a user.”
Keeps context clean
DinoStack is designed around context hygiene.
Heavy investigations, implementation work, and reviews happen in focused contexts. The main session receives structured summaries instead of raw transcript noise.
The result: less context rot during long sessions, less amnesia between sessions, and fewer repeated explanations from the human operator.
Preserves project knowledge
DinoStack can carry forward project-specific conventions, decisions, work-tracking rules, QA instructions, architectural notes, and learned failure modes.
Your agent stops rediscovering the same facts every time it starts a new session.
Plans before it edits
DinoStack helps agents frame the work before they touch the code. For meaningful changes, it defines the goal, constraints, files, risks, success criteria, and verification path.
That means fewer half-built branches, fewer invented requirements, and fewer “almost done” implementations that collapse during review.
Routes work to the right agent
DinoStack treats the main agent like a conductor, not a dumping ground.
Specialist agents handle focused jobs: architecture, implementation, debugging, adversarial review, browser QA, security review, performance analysis, release coordination, documentation, and more.
Each agent gets a narrow brief and clean context. The main thread stays sharp and available for input.
Reviews with teeth
DinoStack includes an adversarial review loop designed to catch flawed assumptions, missing edge cases, and implementation drift.
The reviewer starts fresh. It sees the output and the brief, not the implementer's reasoning trail. That independence makes the review harder to charm and easier to trust.
Critical findings block. Major findings require action or an explicit decision.
Verifies behavior, not vibes
When work changes visible UI or behavioral output, DinoStack can route it through runtime QA.
The QA agent opens the app, navigates, clicks, screenshots, checks browser errors, and reports what actually happened.
“Looks right in code” is not the same as “works for a user.”
Keeps context clean
DinoStack is designed around context hygiene.
Heavy investigations, implementation work, and reviews happen in focused contexts. The main session receives structured summaries instead of raw transcript noise.
The result: less context rot during long sessions, less amnesia between sessions, and fewer repeated explanations from the human operator.
Preserves project knowledge
DinoStack can carry forward project-specific conventions, decisions, work-tracking rules, QA instructions, architectural notes, and learned failure modes.
Your agent stops rediscovering the same facts every time it starts a new session.
DinoStack intelligently escalates by risk
Not every change deserves the same ceremony. DinoStack routes trivial work to direct action, low-risk work to direct execution with a self-check, and anything with an elevated signal to a Worker plus a fresh Skeptic review. When in doubt, the protocol classifies up. Rationalizations to skip the harder path are rejected by design.
How the workflow feels
DinoStack does not turn every task into a ceremony. It applies structure where structure pays for itself.
DinoStack continuously reviews and QAs
Implementation, adversarial review, and runtime QA run as a bounded loop with a hard pass cap. Phase state is written atomically so rate limits or session exits never erase completed work. Every Critical or Major finding ships with a regression test.
Engineer implements
The Worker agent executes the scoped brief in a clean, focused context. Implementation follows the plan and the project's defined conventions.
Skeptic reviews
A fresh Skeptic agent reviews independently — it sees the output and the brief, not the implementer's reasoning trail. Critical findings block. Major findings require action or an explicit decision.
QA verifies in browser
The QA gate verifies in browser or runtime. The agent opens the app, navigates, clicks, screenshots, checks browser errors, and reports what actually happened.
Designed for real development teams
- Risk-aware execution
- DinoStack uses profiles to tune how aggressively work is reviewed. Move faster for low-risk changes. Add stricter gates when correctness matters.
- Parallel fan-out
- Independent work can run in parallel through separate agents. Interdependent work stays sequenced. You get concurrency without turning the project into a pile of conflicting branches.
- Bounded loops
- Implementation, review, and QA loops stay constrained. Failures are tracked. Findings carry forward. The system gets harder to fool as work progresses.
- Project-level instructions
- Every team works differently. DinoStack lets projects define tracking rules, review expectations, QA setup, conventions, and local operating norms.
- Tool-adapted, methodology-first
- DinoStack is designed to work where developers already work. The framework can be adapted to agentic coding environments rather than forcing a separate platform.
Open source core. Commercial depth where teams need it.
DinoStack's core framework is open-source because agentic engineering should be inspectable, extensible, and improved in public.
Space Dinosaurs will continue building commercial offerings around the framework for teams that want deeper support:
Training and enablement
Hands-on onboarding for engineering teams adopting agentic development workflows. We help teams install the framework, tune review profiles, define project instructions, and build durable operating habits.
Implementation support
Guided rollout for teams that want DinoStack integrated into real repos, real workflows, and real delivery systems.
Ecommerce Skill Pack
A closed-source commercial add-on for ecommerce teams. The Ecommerce Skill Pack extends DinoStack with domain-specific agents, workflows, review patterns, and implementation guidance for commerce builds, migrations, conversion work, storefront QA, and revenue-critical changes.
Ask about the Ecommerce Skill PackWho DinoStack is for
DinoStack is for developers and teams who are already using AI coding agents and want more disciplined results.
Use it when:
- Agent sessions get messy after the first hour.
- Complex requests turn into sprawling, unfocused implementation.
- Reviews feel too soft or too dependent on the original agent's assumptions.
- Your AI assistant keeps forgetting project conventions.
- You want parallel agent work without losing control.
- You need a repeatable operating model your whole team can share.
- You care about shipping faster without quietly lowering your engineering bar.
Built for the next phase of software development
The next advantage in AI-assisted engineering will not come from asking agents to write more code.
It will come from better operating discipline.
DinoStack gives your agents a protocol for planning, delegation, review, verification, memory, and recovery. It turns raw model capability into a system developers can trust, inspect, and improve.