Persistent AI Research Agent/v0.9-alpha

Research that never really ends.

DeepScout continuously investigates the topics you care about, builds an evidence graph, detects conflicting information, and keeps your research updated as the world changes.

Continuous reconciliation·Deterministic citations·Multi-agent DAG
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workspace://deepscout-prod
Researching
Research Project·v1.4-active

AI Coding Agents MarketAutonomous DAG

Crawling ArXiv:2409.11 — Autonomous Code Synthesis benchmark updates
142
sources analyzed
+14 today
38
claims verified
94.2% confidence
4
contradictions detected
2 reconciled
12m
last updated
next sync: 3m
Active Research Branches
8 active threads
#1
Currently synthesizing: Market size
New claim verified: 'Autonomous agents reduce average PR cycle time by 42%' (ArXiv:2409.11)
Deterministic Proof
The Paradigm Shift

Traditional AI research ends when the answer is generated.

Research shouldn't restart from zero every time you ask a question. DeepScout maintains a persistent research workspace where sources, claims, evidence, open questions, and conclusions evolve over time.

Traditional Chatbot / Search AI

Static & Ephemeral
01
Ask
User prompts once
02
Search
One-off web crawl
03
Generate report
Prose text summary
04
Forget everything
Context discarded
Result: Stale output within 48 hours. Zero change tracking.

DeepScout Persistent AgentPERSISTENT

Continuous & Living
01
Define a goal
Set research scope & hypotheses
02
Research
Deep multi-source investigation
03
Store evidence
Structured knowledge graph
04
Monitor changes
Continuous differential crawler
05
Update conclusions
Live synthesis & alerts
Result: An ever-evolving intelligence baseline with verifiable lineage.Loop frequency: Continuous diffing
Structured Knowledge

Every conclusion is backed by evidence.

DeepScout doesn't just generate prose. It stores structured relationships between claims and the evidence supporting or contradicting them.

Evidence Lattice ID: #EV-8842-AUTONOMY
Confidence: High (94%)Last verified: Today, 14:12 UTC
Evaluated ClaimGraph Node: Root Claim

“AI coding agents are increasingly moving toward autonomous multi-step workflows.”

Supporting Evidence (4 Primary Citations)4 nodes active
Benchmark

Anthropic Research

Long-horizon agent execution & computer use evaluations (Oct 2024)

Telemetry

GitHub Activity

410% YoY increase in agentic repo commits & automated pull requests

Survey

Developer Surveys

Stack Overflow & State of AI Devs: 68% shift toward multi-file reasoning

Market Data

Product Releases

Tool-use & autonomous agent rollouts across major IDEs & CLIs

Counter-Evidence2 tracked

Context Window Degradation

Diminishing returns in multi-turn plans past 40 tool invocations without pruning

Enterprise Compliance Guardrails

Strict sandbox limits preventing auto-merge in production financial systems

Entities: Claim ↔ Source ↔ Evidence ↔ Counter-evidence ↔ Conclusion
Consensus: High confidence multi-factor verification
Applications

Built for teams that rely on deep factual truth.

From corporate development to engineering leadership, DeepScout replaces fragmented bookmarks and forgotten Google Docs with persistent truth engines.

24/7 Competitor Radar

Competitive Intelligence

Continuously track competitors, pricing, product launches, hiring, funding, community sentiment, and strategy changes.

Track subtle pricing & packaging changes
Monitor key engineering departures & hiring
Sentiment shifts on X, Reddit, HN
Living Market Maps

Market Research

Build continuously updated market maps backed by verifiable sources, eliminating outdated analyst PDFs.

Dynamic TAM / SAM projections
Category taxonomy evolution
Citations for every market estimate
Ecosystem Telemetry

Technology Research

Track frameworks, AI models, APIs, benchmarks, GitHub projects, and emerging technologies as they release.

Weekly benchmark shifts (SWE-bench, HumanEval)
Dependency & repo velocity tracking
Breaking API & architecture shifts
Living Thesis Graph

Strategic Research

Maintain long-running research projects for investment, product strategy, or executive company decisions.

Investment thesis stress-testing
Invalidated assumption alerts
Executive briefings backed by source logs
Workspace Deep Dive

A command center for living knowledge.

A multi-threaded investigation environment where projects stay alive for months. Filter by branches, inspect raw sources, or inspect the underlying evidence graph.

/deepscout-core // session_3902
Daemon Active
Research Projects
Storage:41.8 MB graph
Recrawl rate:Every 4h
Reasoning engine:Claude 3.5

AI Coding Agents

Root entity: Autonomous Software Engineering Systems
Sources
184
+18 this week
Verified Claims
52
100% cited
Open Questions
11
Under investigation
Contradictions
6
4 resolved
Confidence
87%
High consensus
Researching now
Depth: Level 3 Recursive

Investigating enterprise adoption of autonomous coding agents

Analyzing enterprise security policy reports and SOC2 compliance blockers...

Sources being analyzed right now:
GitHub
Repo commits & enterprise issues
Hacker News
Front-page discussions & comments
Company blogs
Engineering posts & architecture notes
Technical documentation
API specs & enterprise whitepapers
Recent Knowledge Graph AssertionsView all 52 claims
Multi-agent orchestration requires shared memory architectures
ACM Queue & IEEE Software·96%· 18m ago
Self-healing test generation reduces QA bottlenecks by 31%
Engineering blog & case studies·89%· 44m ago
Engine & Reasoning·Powered by Claude

Built for deep, multi-step reasoning.

Claude is used as the reasoning engine behind DeepScout's research workflow. It orchestrates long-horizon planning, synthesizes unstructured technical sources, cross-references conflicting claims, and maintains contextual integrity over extended investigation lifecycles.

Plan research01

Deconstructs broad inquiries into multi-layered hypotheses and execution DAGs.

Understand long documents02

Ingests entire 200-page regulatory filings, whitepapers, and dense technical specs without degradation.

Compare sources03

Cross-checks methodologies, sample sizes, and underlying definitions across dozens of studies.

Identify contradictions04

Detects numerical divergence, temporal discrepancies, and conflicting consensus assertions.

Maintain research context05

Remembers past findings and constructs an ever-growing hierarchical evidence lattice.

Decide what to investigate next06

Dynamically questions its own conclusions and initiates new crawler probes to resolve blindspots.

“DeepScout combines Claude's frontier reasoning with a persistent, deterministic evidence graph.”

Deterministic Provenance·Zero Hallucinated Consensus·Long-horizon Memory
Currently in development·Private Alpha

Join the future of autonomous research.

DeepScout is an early-stage MVP being built to explore persistent AI research workflows. We're actively onboarding initial design partners, research teams, and engineering leaders.

No spam. Early access invitations are sent in small curated batches.
Initial version focuses on:
Autonomous web research with recursive verification
Persistent research projects that track topic drift
Structured evidence tracking and provenance graphs
Automated contradiction detection across disparate sources
Differential research timelines and trend alerting
Source-backed synthesized reports with verifiable citations