Product name: Spark Intelligence
Repo name: spark-intelligence-builder
Version: v1
Mode: 1:1 persistent agent runtime
Spark Intelligence is a Spark-native agent product where each user interacts with one persistent agent identity over time.
That agent is not a generic wrapper chatbot. It is a Spark-runtime agent powered by:
Spark Researcheras the runtime coreSpark Swarmas the multi-agent execution and delegation layerDomain chipsas specialization modulesSpecialization pathsas long-term growth tracksAutoloop flywheelsas improvement loops- Channel adapters such as
Telegram,WhatsApp, andDiscord
The winning product shape is no longer a one-shot assistant. It is a persistent agent the user keeps returning to.
What users want:
- one agent identity
- continuity across sessions
- the same agent reachable through multiple channels
- visible specialization over time
- real work output, not only chat
Spark Intelligence should win by making that shape native to the Spark ecosystem instead of building a disconnected agent shell.
Current popular agent products are usually strong on surface UX and weak on internal structure.
Common failures:
- the agent has no real specialization model
- memory is bolted on and noisy
- multi-agent execution is opaque
- prompt changes break behavior without governance
- channels are adapters to a thin bot, not to a real evolving system
- there is no clean path from user interaction to better specialization
Spark already has the harder pieces:
- runtime intelligence
- swarm coordination
- domain specialization
- recursive improvement paths
Spark Intelligence should package those pieces into a single product a user can actually live with.
Spark Intelligence is a persistent 1:1 agent runtime where the user talks to one evolving Spark-native agent across channels, while the agent improves through domain chips, specialization paths, swarm execution, and autoloop flywheels.
The user should feel they are talking to the same agent each time, regardless of channel.
The runtime core should come from Spark Researcher, not from a separate parallel agent stack.
The agent should become stronger by attaching and evolving domain chips, not by becoming a vague generalist.
Telegram, WhatsApp, and Discord are access surfaces, not separate products.
Memory policy, promotion, retrieval, and hygiene belong to the memory domain chip, not to the core ownership of this repo.
The product should create outputs, route tasks, activate tools, and visibly evolve.
Primary early user:
- ambitious builder
- solo founder
- operator
- technical creator
- agent-native user who wants one persistent system instead of many fragmented tools
Early user expectations:
- wants leverage, not entertainment
- wants continuity
- wants a real working system they can shape
- wants specialization they can feel
- wants to interact through familiar channels
Users hire Spark Intelligence to:
- act as their persistent agent counterpart
- route work to the right specialized capabilities
- evolve with their goals
- keep context across time and channels
- execute with tools and swarm support when needed
- become more useful as specialization deepens
- one persistent Spark-native agent identity per user
- cross-channel delivery adapters
- runtime built on
Spark Researcher - delegation and multi-step execution through
Spark Swarm - chip attachment and chip-based specialization routing
- specialization path selection and progression
- autoloop hooks for iterative improvement
- operator-facing controls for runtime state and capability visibility
- building a separate non-Spark runtime
- generic “AI friend” positioning
- owning memory doctrine inside this product
- trying to support every consumer use case
- broad marketplace dynamics before core runtime quality exists
Spark Researcher
Responsibilities:
- core reasoning loop
- execution planning
- task state ownership
- user-facing agent continuity
Spark Swarm
Responsibilities:
- delegation
- sub-agent execution
- specialization fan-out
- parallel work orchestration
Domain chips
Responsibilities:
- provide domain-specific heuristics and workflows
- shape how the persistent agent behaves in specific domains
- expose specialized capability surfaces
Specialization paths
Responsibilities:
- define how an agent grows stronger over time
- determine what domain depth gets added next
- create progression rather than random capability sprawl
Autoloop flywheels
Responsibilities:
- convert usage into better future performance
- drive iterative improvement and refinement
Telegram, WhatsApp, Discord, and future adapters
Responsibilities:
- session entry
- identity continuity
- message transport
- notifications and re-engagement
Owned by the separate memory domain chip.
This repo depends on that layer but does not define its doctrine.
The user creates or activates their Spark Intelligence agent.
They choose:
- name / identity defaults
- initial specialization bias
- preferred channel surfaces
- first specialization path
The user messages the same agent through their preferred channel.
The agent:
- responds in a continuous identity
- routes work internally
- uses specialization where relevant
- invokes swarm support when the task requires more depth
- returns outputs that feel tied to the same evolving agent
Over time, the agent:
- becomes stronger in attached domains
- reflects specialization path progression
- improves via flywheel loops
- stays coherent as one agent rather than fragmenting into many bots
- persistent user-to-agent identity mapping
- Spark Researcher-backed runtime session
- Spark Swarm task delegation
- domain chip routing
- specialization path assignment
- channel adapters for at least one text surface, ideally
Telegramfirst - operator visibility into active chips, active path, and runtime status
- multi-channel identity continuity
- task history and execution trace visibility
- chip attachment and detachment controls
- swarm escalation rules
- lightweight agent profile surface
- rich voice surfaces
- proactive notifications
- agent marketplace or sharing
- consumer-scale onboarding flows
The product should treat domain chips as attached specialization surfaces for one persistent agent.
The main moat is runtime quality, specialization, and evolution, not cosmetic personality tuning.
Users should stay because their agent is genuinely growing through the Spark ecosystem:
- better specialization
- better loops
- better execution
- better continuity
Maps user identity across channels to one Spark-native agent.
Determines which domain chips are active for a given task or user profile.
Tracks which growth path the agent is following and what capability deepening should happen next.
Decides when the main agent should delegate into Spark Swarm.
Supports inbound and outbound messaging across text surfaces.
Lets operators inspect active state, specialization, and execution health.
- weekly active persistent agents
- messages per active agent
- returning users per agent
- cross-channel continuity rate
- percentage of sessions that invoke specialized capabilities
- percentage of tasks successfully completed via Spark runtime
- domain chip attachment rate
- specialization path activation rate
- swarm escalation success rate
- flywheel improvement rate
- agent coherence across channels
- specialization accuracy
- task completion quality
- operator trust in runtime state
If Spark Intelligence is framed as “an AI that does everything,” it loses its edge.
If memory ownership is mixed into this product instead of delegated to the memory chip, boundaries will blur.
If all adapters are pursued at once, runtime quality will degrade.
If chip attachment is only branding and not real behavior change, users will notice quickly.
Start with:
- one persistent Spark Intelligence agent
- one primary adapter, preferably
Telegram - Spark Researcher as runtime core
- Spark Swarm for hard tasks
- a small number of high-signal domain chips
- one or two specialization paths
This is enough to prove the shape without overextending.
Spark Intelligence is not “another AI assistant.”
It is:
- a persistent Spark-native agent
- delivered through familiar channels
- specialized through domain chips
- deepened through specialization paths
- improved through autoloop flywheels
- coordinated through Spark Swarm
The user talks to one agent, but that agent is backed by the full Spark ecosystem.
- Which channel should be the first launch surface:
Telegram,WhatsApp, orDiscord? - Which 3-5 domain chips should be allowed in the first public runtime?
- How visible should specialization paths be to the user versus the operator?
- What is the minimum operator control surface needed for trust?
- When should the main agent escalate to swarm versus stay single-agent?
- architecture spec
- channel adapter spec
- identity and session model spec
- provider and auth config spec
- Spark Researcher integration contract
- chip attachment spec
- specialization path spec
- operator control surface spec