S
swebench.com·this weekSWE-bench Verified: Real-World Agent Performance Leaderboard
Top closed models resolve 55–65% of real GitHub issues end-to-end; leading open-weight agents trail at 28–34%.
Cred
96
Auth
94
Fresh
94
Rel
88
Reasoning Engine
Conducting research…
- Discovering sourcesIdentified 6 candidate sources across 4 publication types.
- Analyzing sourcesExtracted atomic claims; scored credibility, recency, and bias on each.
- Cross-referencingDetected contradictions and reconciled overlapping claims.
- Synthesizing findingsCompressed claim graph into structural themes and a working thesis.
- Generating intelligenceDrafted executive brief, evidence map, risks, and recommendations.
Research Telemetry
live · demoReasoning
84/100
Confidence
86/100
Evidence
88/100
Depth
78/100
Diversity
82/100
Synthesized Answer
Identify computer-use security risks
Computer-use agents inherit every attack surface of a logged-in human plus new ones: prompt injection from rendered content, click-jacking via DOM manipulation, exfiltration through agent-controlled browser sessions, and credential blast radius across SaaS apps.
Key Points
- Top risk: indirect prompt injection from rendered web/PDF content
- Most agents run over-scoped — full user OAuth, not least-privilege
- Action firewalls + per-domain credentials are the strongest controls
- SOC tooling cannot replay agent sessions — major gap
- Regulated industries must red-team the runtime, not just the model
Knowledge Graph
10 nodes · 11 edgestopicconceptcompanyentity
Auto-generated Insights
Trend
Vertical agents are out-scaling horizontal copilots in revenue per seat by 3–5×.
Contradiction
Benchmarks show 60% task completion; production deployments report 25–40% — eval-set leakage suspected.
Finding
Computer-use APIs cut integration timelines from months to weeks for legacy systems.
Signal
Eval/observability startups (Braintrust, Langfuse, Arize) are seeing the fastest ARR growth in the stack.
Structured Data
Enterprises with agents in production
31%
18pp YoYGartner Q1 2026
SWE-bench Verified (top closed)
65%
22pp YoYreal GitHub issue resolution
Vertical agent gross margin
72%
9pp YoYmedian across 40 disclosed startups
Inference cost per agent task
$0.18
−54% YoYblended, 30-min analyst-equivalent
Sources6 ranked
Sorted by relevance
Research Paper
A
a16z.com·this weekThe Vertical Agent Thesis
Workflow-specific agents in legal, sales, and finance are achieving 40–70% task automation with bounded eval surface.
Cred
84
Auth
85
Fresh
92
Rel
88
A
anthropic.com·this weekComputer Use, One Year In: What Actually Works
Screen-reading + structured action agents now handle multi-app workflows; reliability gates remain the production bottleneck.
Cred
92
Auth
90
Fresh
96
Rel
88
A
arxiv.org·this weekGAIA: A Benchmark for General AI Assistants
Human performance on GAIA is 92%; best agent score is 49%. Long-horizon planning is the dominant failure mode.
Cred
97
Auth
96
Fresh
80
Rel
88
G
gartner.com·this weekEnterprise Agent Adoption Survey, Q1 2026
31% of enterprises have at least one agent in production; 78% cite evaluation tooling as the #1 blocker to scale.
Cred
91
Auth
93
Fresh
90
Rel
88
H
huggingface.co·this weekLangGraph vs Native SDKs: A Framework Comparison
Open frameworks lead on portability; native SDKs lead on tool fidelity and latency. Convergence expected within 12 months.
Cred
84
Auth
85
Fresh
95
Rel
88
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Demo mode · All sources, insights, and data are mock-generated for illustration.