Active Agents
47
Across 5 pattern types
Workflows Run Today
284
+34% vs yesterday
Tokens Saved
2.1M
Via model tiering
Guardrail Events
12
3 escalated to human
Quick Actions
🤖 Active Agent Status
Real-time agent health across all 5 pattern types
ReAct Agents (18)Running
Planning Agents (11)Running
Reflection Agents (8)Processing
Multi-Agent (7)Running
Sequential (3)Idle
📡 Live Workflow Feed
Recent agent tasks and completions
The Three Layers — Claude Code as Engineering Infrastructure
🧠 Foundation Layer
Memory & Context
CLAUDE.md · Permissions · Context · Compaction · Checkpoints.
Before: 15 mins/session re-explaining context. After: 0 mins.
Before: 15 mins/session re-explaining context. After: 0 mins.
Foundation Layer
🛡 Control Layer
Trust & Verification
Plan Mode · Hooks · Skills · Tool guardrails · Human-in-loop.
Before: Constant "did I break something?" anxiety. After: Clear plan, reviewed before execution.
Before: Constant "did I break something?" anxiety. After: Clear plan, reviewed before execution.
Control Layer
🚀 Scale Layer
Team Infrastructure
MCP · Subagents · Headless Mode · Slash Commands · CI automation.
Before: Manual, repetitive tasks eat your week. After: Automated with slash commands + headless.
Before: Manual, repetitive tasks eat your week. After: Automated with slash commands + headless.
Scale Layer
Foundation Layer — Memory & Context
01 / 12
📄 CLAUDE.md
Project conventions file loaded automatically every session. Stack, rules, blocked commands. Zero re-explaining.
Foundation
02 / 12
🔒 Permissions
Allowlist safe tools, block risky commands.
rm -rf, git push --force, DROP TABLE blocked by default.Foundation
03 / 12
🪟 Context Control
Precise control over what Claude sees in the active window. Right information at the right time, nothing more.
Foundation
04 / 12
🗜 Compaction
Compress long conversations while preserving critical context. Never lose state mid-task on complex sessions.
Foundation
05 / 12
📸 Checkpoints
Auto-snapshots at every step. Instant rollback to any prior state. No more "what did I just break?" moments.
Foundation
Control Layer — Trust & Verification
06 / 12
🗺 Plan Mode
Claude proposes a full execution plan. You approve, edit, or reject before a single file is touched. Trust built-in.
Control
07 / 12
🪝 Hooks
Trigger custom scripts before/after tool use, on notifications. Run lint after every edit automatically.
Control
08 / 12
📦 Skills
Reusable instruction sets for recurring tasks.
/pr-desc analyzes diff, writes summary, flags breaking changes.Control
Scale Layer — Team Infrastructure
09 / 12
🔌 MCP
Model Context Protocol — connect Claude to external systems: GitHub, Slack, Notion, Jira, Linear, Vercel. One integration standard.
Scale
10 / 12
🤖 Subagents
Spawn parallel agents for multi-step tasks. Research + design + implement + test — all simultaneously across 5 agents.
Scale
11 / 12
⚙️ Headless Mode
Run Claude non-interactively in scripts, CI, or cron. Nightly dependency updates, PR generation, test runs — while you sleep.
Scale
12 / 12
💬 Slash Commands
/deploy-staging · /generate-migration · /scaffold-api. One command triggers full automated pipeline.Scale
$ /deploy-staging
→ Reading CLAUDE.md conventions...
→ Plan Mode: 5-step deploy plan generated
1. Build: npm run build (Next.js 14)
2. Test: jest --coverage (min 80%)
3. Lint: eslint src/ (0 errors)
4. Deploy: vercel --env staging
5. Notify: slack #deployments
[Approve] [Edit] [Reject]
→ Checkpoint saved: pre-deploy-20260518
→ ✓ All hooks passed · Deploy complete
→ Reading CLAUDE.md conventions...
→ Plan Mode: 5-step deploy plan generated
1. Build: npm run build (Next.js 14)
2. Test: jest --coverage (min 80%)
3. Lint: eslint src/ (0 errors)
4. Deploy: vercel --env staging
5. Notify: slack #deployments
[Approve] [Edit] [Reject]
→ Checkpoint saved: pre-deploy-20260518
→ ✓ All hooks passed · Deploy complete
ReAct
18
Adaptive agents
Planning
11
Structured agents
Reflection
8
Quality agents
Multi-Agent
7
Coordinator teams
Sequential
3
Pipeline agents
ReAct Agents — Iterative Reasoning + Tool Use
Debug Agent
Iteratively hypothesizes and tests fixes for bugs. Shifts approach based on test output. Unknown path until solved.
Running · 3 loops
ReActCodebase Explorer
Navigates unfamiliar repositories. Discovers patterns, dependencies, and architecture through iterative file reads.
Running · 7 tool calls
ReActCustomer Support AI
Branches based on user input. Queries knowledge base, logs tickets, escalates to human when confidence is low.
Processing · awaiting input
ReActResearch Agent
Follows new evidence. Searches docs, PRs, issues, and logs to synthesize answers that couldn't be planned upfront.
Running · RAG active
ReActAlert Triage Agent
Responds to monitoring alerts, investigates root cause, resolves or wakes human if it's genuinely critical.
Running · 211 alerts
ReActSecurity Audit Agent
Pen testing, red-team exercises, vulnerability scanning. Tool for hardening, not for trusted security decisions.
Idle · scheduled 02:00
ReActPlanning Agents — Structured Execution
Feature Builder
Design → Implement → Test → PR. Known structure, ReAct inside each step for local uncertainty handling.
Running · step 2/5
Planning + ReActOnboarding Agent
Create accounts → configure env → send welcome → assign manager → schedule orientation. Fixed predictable steps.
Running · step 3/5
PlanningPR Description Agent
Analyze diff → identify components → write summary → list breaking changes → add test notes. Triggered by /pr-desc.
Processing · analyzing diff
PlanningReflection Agents — Generate → Critique → Refine
Code Review Agent
Generates review, critiques own output against style guide and correctness criteria, refines before posting.
Running · critique pass 2
ReflectionTest Quality Agent
Writes tests, evaluates coverage and edge cases, refines until evaluation criteria pass. High cost of missing tests.
Processing · refining
ReflectionDoc Writer Agent
Generates docs, runs readability and accuracy critique, refines. Client-facing output warrants extra pass.
Idle · queued
ReflectionMulti-Agent Teams — Specialization at Scale
Feature Squad (5)
Research → Schema → API → UI → Tests in parallel. Context too large for one agent. Each specialist owns one domain.
Running · all 5 active
Multi-AgentDep Updater (Cron)
Nightly: check updates → run tests → open PR → tag reviewers. Headless. Runs while you sleep. Zero human time.
Running · cron 02:00
Multi-Agent + HeadlessDeploy Pipeline (3)
Build agent → Test agent → Deploy agent. Sequential multi-agent with Temporal-style durable execution.
Idle · trigger on merge
Multi-Agent + SequentialLive Workflows — Click Run to Simulate
🔄 Nightly Dependency Updater
Headless · Cron 02:00 · Multi-agent · ReAct inside each step
Automated
Check npm outdated
→
Filter breaking changes
→
Run test suite
→
Open PR
→
Tag reviewers
→
Notify #deps
🏗 Feature Scaffold — /scaffold-api
Slash Command · Planning + ReAct · CLAUDE.md conventions
On-demand
Parse schema
→
Generate CRUD routes
→
Add validation
→
Write tests
→
Run lint+typecheck
→
Open PR
🪞 Code Review + Reflection Loop
Reflection Pattern · Generate → Critique → Refine · Hook on PR open
Hook-triggered
Read PR diff
→
Generate review
→
Self-critique
→
Meets criteria?
→
Refine if needed
→
Post review
🚀 Deploy to Staging — /deploy-staging
Sequential Pattern · Plan Mode · Checkpoint at each step
Plan Mode
npm run build
→
jest --coverage
→
eslint (0 errors)
→
vercel --staging
→
Notify Slack
🎯 Feature Squad (5 Subagents Parallel)
Multi-Agent · Parallel execution · Coordinator routes
Multi-Agent
Agent 1
Research patterns
Research patterns
Agent 2
Design schema
Design schema
Agent 3
Write API
Write API
Agent 4
Build UI
Build UI
Agent 5
Write tests
Write tests
5-Question Decision Tree — Choose Your Agentic Pattern
Answer these questions in order to find your starting pattern:
Q1: Is the solution path known in advance?
Can you define the full step-by-step process before execution begins? (e.g. invoice processing, onboarding flows)
✓ Yes — path is clear and predictable
✗ No — path emerges from execution
All 5 Patterns — When to Use Each
Sequential
Sequential / Structured Workflow
Fixed, predictable steps. Same process every time. Use LLM only for interpretation/generation — deterministic code handles the rest.
Fast, predictable, cost-efficient
Avoid: ReAct loops where steps are already defined
Breaks on edge cases not in original spec
ReAct
ReAct — Reason, Act, Observe, Repeat
Unknown solution path. Each step depends on prior output. Debugging, research, customer support, alert triage.
Flexible — adapts to new information dynamically
Best default for most real-world tasks
Can loop excessively without clear stopping condition
Planning
Planning + ReAct Execution
Structure is articulable upfront but each step needs adaptive reasoning. Feature builds, research reports, provisioning.
Exposes dependencies early, avoids mid-run surprises
Plans can become outdated — rigid when conditions differ
Reflection
Single Agent + Reflection (Generate → Critique → Refine)
High quality output required. Clear evaluation criteria. Cost of error is high (deployed code, client docs, SQL queries).
Improves correctness beyond first-pass output
Adds latency and cost — skip when speed > quality
Multi-Agent
Multi-Agent Specialist System
Task exceeds one context window, or needs different reasoning styles across stages (legal + financial + coding).
Parallelism, domain expertise per agent
Coordination overhead, shared state complexity, more failure points
Pitfalls
Common Failure Signals
ReAct looping excessively → Needs planning or clearer stop condition
Planning agent abandons plan → Switch to lightweight planning + ReAct
Reflection not improving output → Evaluation criteria unclear; critic too aligned
Multi-agent routing failures → Use deterministic routing for predictable cases
Planning agent abandons plan → Switch to lightweight planning + ReAct
Reflection not improving output → Evaluation criteria unclear; critic too aligned
Multi-agent routing failures → Use deterministic routing for predictable cases
ReAct Pattern — Iterative Reasoning + Action
🔄 How ReAct Works
ReAct alternates between Reason (analyze what to do next), Act (invoke a tool), and Observe (use the result). This loop continues until a stopping condition is met. It does not require knowing the full path upfront — each step is decided based on what was just observed.
REASON → TypeError in user auth. Check login.ts first
ACT → read_file('src/auth/login.ts')
OBSERVE → ErrorWrapper not imported. Missing import statement
REASON → Need to check ErrorWrapper definition in types.ts
ACT → read_file('src/auth/types.ts')
OBSERVE → ErrorWrapper defined, ErrorCode enum missing
REASON → Add ErrorCode enum and fix import in login.ts
ACT → edit_file, run_tests()
OBSERVE → All 47 tests passing
DONE → Bug resolved in 4 loops
ACT → read_file('src/auth/login.ts')
OBSERVE → ErrorWrapper not imported. Missing import statement
REASON → Need to check ErrorWrapper definition in types.ts
ACT → read_file('src/auth/types.ts')
OBSERVE → ErrorWrapper defined, ErrorCode enum missing
REASON → Add ErrorCode enum and fix import in login.ts
ACT → edit_file, run_tests()
OBSERVE → All 47 tests passing
DONE → Bug resolved in 4 loops
✓ Use when
• Unknown solution path
• Steps depend on prior output
• Debugging, research, customer support
• Alert triage, exploration tasks
• Best default for most real-world tasks
• Steps depend on prior output
• Debugging, research, customer support
• Alert triage, exploration tasks
• Best default for most real-world tasks
✗ Watch out for
• Excessive looping without progress
• No clear stopping condition
• Using ReAct when path is already known
• Over-calling tools on already-known facts
→ Fix: add hard loop limit (3–5 max)
• No clear stopping condition
• Using ReAct when path is already known
• Over-calling tools on already-known facts
→ Fix: add hard loop limit (3–5 max)
Planning Pattern — Decompose Before Executing
🗺 How Planning Works
Planning first Analyzes the task, then Decomposes it into ordered subtasks, Sequences dependencies, then Executes with ReAct inside each step. Exposes dependencies early — prevents mid-execution surprises from hidden complexity.
ANALYZE → Task: Add user profile feature
DECOMPOSE → 5 subtasks identified
1. Research existing user patterns in codebase
2. Design DB schema (users table extension)
3. Write API endpoints (GET/PATCH /api/users/:id)
4. Build React ProfilePage component
5. Write jest tests (min 85% coverage)
SEQUENCE → 1→2→3→4→5 (linear dependencies)
[Approve] [Edit] [Reject]
EXECUTE → Step 1 starting... (ReAct inside)
DECOMPOSE → 5 subtasks identified
1. Research existing user patterns in codebase
2. Design DB schema (users table extension)
3. Write API endpoints (GET/PATCH /api/users/:id)
4. Build React ProfilePage component
5. Write jest tests (min 85% coverage)
SEQUENCE → 1→2→3→4→5 (linear dependencies)
[Approve] [Edit] [Reject]
EXECUTE → Step 1 starting... (ReAct inside)
Reflection Pattern — Generate → Critique → Refine
🪞 How Reflection Works
After generating output, a critic evaluates it against explicit criteria. If it doesn't meet the bar, it revises. This loop repeats until criteria pass. Key: the critic must be independent from the generator — otherwise it mirrors rather than evaluates.
GENERATE → Code review for PR #247 written
CRITIQUE → Evaluating against review criteria...
✗ Missing: security implications of auth change
✗ Missing: test coverage for error paths
✓ Logic correctness: pass
✓ Style guide compliance: pass
REFINE → Adding security and test coverage notes...
CRITIQUE → All 4 criteria pass
DONE → Review posted to PR #247
CRITIQUE → Evaluating against review criteria...
✗ Missing: security implications of auth change
✗ Missing: test coverage for error paths
✓ Logic correctness: pass
✓ Style guide compliance: pass
REFINE → Adding security and test coverage notes...
CRITIQUE → All 4 criteria pass
DONE → Review posted to PR #247
Multi-Agent — Specialization at Scale
🕸 When Multi-Agent Makes Sense
Only use when: (1) the task exceeds one context window, or (2) different stages require clearly different reasoning styles. The trigger should be a clear bottleneck — not architectural preference. Coordinator routes; specialists execute. Never peer-to-peer.
COORDINATOR → Task: "Add user profile feature" → decomposing
Agent 1 → Research existing patterns in codebase
Agent 2 → Design database schema
Agent 3 → Write API endpoints
Agent 4 → Create React components
Agent 5 → Write test suite
[All 5 running in parallel]
COORDINATOR → Synthesizing outputs... PR #251 created
Agent 1 → Research existing patterns in codebase
Agent 2 → Design database schema
Agent 3 → Write API endpoints
Agent 4 → Create React components
Agent 5 → Write test suite
[All 5 running in parallel]
COORDINATOR → Synthesizing outputs... PR #251 created
AI-native SDLC — How the Best Teams Are Restructuring Work
Based on insights from Microsoft CVP Tim Bozarth, 1Password CTO Nancy Wang, and Atlassian CTO Taroon Mandhana at DX Annual 2026. Historically 80% of engineering time went to operate. The most effective AI-native teams are inverting that ratio.
Plan
▲ Human
Prototypes replace PRDs. Alignment & decision-making are the bottleneck, not building.
Create
▲ AI
AI is very good at this. Already compressing fast. Squads of 3–4 instead of 8.
Validate
▲ Human
Don't delegate to AI yet. Humans as tastemakers. Craft and judgment matter most.
Deploy
▲ AI
Automated pipelines, headless agents, slash commands. Minimal human in loop.
Operate
▲ AI (fast)
Most untapped potential. Agents respond to alerts, run post-incident reviews, patch vulns.
What's Actually Changing
Atlassian · Taroon Mandhana
Squads of 3–4 people for zero-to-one projects — would have felt too small a year ago. AI compressed the building part enough that the bottleneck is now alignment and decision-making.
Microsoft · Tim Bozarth
8-week cycles with small, mission-specific v-teams. Form around a specific question, give them 8 weeks, then decide: continue, absorb, or stop. Speed of learning over sustained delivery.
1Password · Nancy Wang
Planning horizons compressed from 12–18 months to a single quarter. Stopped writing full-length PRDs — teams build prototypes and put them in front of customers instead.
Non-Engineers Contributing Code
Atlassian · Taroon Mandhana
Designers submitting PRs. Prototyping by non-engineers is a clear unlock. Shipping to production is a different bar. Teams with robust test suites are most comfortable accepting these contributions.
1Password · Nancy Wang
CX associates generating PRs for front-end test coverage. Engineering's role shifted: building testing harnesses and review processes to evaluate contributions — higher leverage work.
Microsoft · Tim Bozarth
Non-engineers using AI to optimize how they work: gathering information, communicating, running workflows. That goes to production in how the business operates — not just in code.
Org Design Insights — AI-native Engineering Organizations
Adoption & Culture
Microsoft · Tim Bozarth
Track daily active AI use across the org. Low adoption in a team is a diagnostic signal — something is blocking them. Go figure out what it is. "We don't set targets for usage itself. The metrics we care about are speed, ease, and quality."
Atlassian · Taroon Mandhana
Organic AI champions in groups of 100–200 engineers. Someone naturally emerges who's excited and starts showing others. Amplifying those wins is far more effective than any top-down mandate.
1Password · Nancy Wang
Built a guild of AI champions. When someone uses AI to pull a launch date forward by two weeks, tell that story publicly. People see the result and want to figure out how to get there themselves.
Skills Profile: Great Engineer in 3–5 Years
Microsoft · Tim Bozarth
Maker's mindset — not attached to a specific tool, oriented toward an objective, driving toward it with whatever is available. Not just writing code faster. Making better decisions about what to build.
1Password · Nancy Wang
Generalists with strong product instincts. Lines between product and engineering are blurring. Span the full SDLC. Don't specialize too early. Operate at a higher level of abstraction.
Atlassian · Taroon Mandhana
Agency is becoming as important as technical depth. The willingness to step up, have the right conversations, make decisions without waiting to be told. When AI compresses building time, the differentiator is who figures out what to build next.
Tech Debt & Code Quality Risks
Atlassian · Taroon Mandhana
Patterns of duplication and tech debt increasing as people quickly produce features. Maintainability is suffering. Prompted a return to standardized approaches and more right-of-code quality checks.
Microsoft · Tim Bozarth
50% of simple vulnerabilities at Atlassian — library version bumps — now resolved using AI. Accessibility bugs getting done faster. Run in the background by central dev infra teams to give time back to engineers.
What Not to Delegate to AI
✗ Don't delegate Validate. Humans still need to be in the loop for important systems. AI is good at creating; not yet at judging correctness.
✗ Don't delegate Security. Use AI for pen testing and red teams to battle-harden — don't trust it to deliver secure products on its own.
✗ Don't mandate AI usage. Top-down mandates are less effective than organic champions and public celebration of wins.
✗ Don't plan beyond 90 days. Anything beyond one quarter is guesswork when tools and capabilities change this fast.
Tokens Today
4.2M
Across 47 agents
Est. Cost
$8.40
Tiered model pricing
Cost/Workflow
$0.03
After tiering + cache
Human Escalations
3
< 1% exception rate
📊 Token Cost by Layer
Where tokens are actually spent per workflow run
LLM Inference (tiered models)40%
Human review (exceptions)20%
State + infra (Temporal)15%
RAG retrieval + embedding15%
Tool calls / APIs10%
💡 Cost Reduction Playbook
Tactics from 1Password, Atlassian, Microsoft
Model Tiering — Use Sonnet for orchestration, Haiku for retrieval. Cuts cost 60–70% with no quality loss.
Semantic Caching — Cache RERA/GST lookups. Same regulatory query = zero tokens. 35% reduction.
Context Compression — Pass structured summaries between agents, not raw outputs. Stops context inflation.
Loop Limits — Hard ceiling of 3–5 loops per agent. No runaway ReAct spirals.
Negotiate Volume — 1Password: commit to volume with model providers. Significantly reduces per-token cost.
Atlassian · Taroon Mandhana
"I'm on my third budget forecast since January. Token costs are volatile, and the models and pricing are shifting underneath you constantly. We literally started to think about managing it like AWS COGS cost because it requires that level of rigor and sophistication."
1Password · Nancy Wang
Built an internal SaaS cost management tool that maps token spend by repo and project. "Without that visibility, you're flying blind." Maps token spend back to intent — so you know what tokens are actually being spent on. "Treat your AI token bill the same way you'd negotiate a cloud contract."
Agents Live
47
All patterns active
LLM Calls / hr
1,284
Across all agents
Guardrail Events
12
3 escalated
Avg Latency
340ms
p50 across agents
📡 Live Agent Trace
Real-time agent calls, token costs, guardrail events
💰 Cost Attribution by Agent
Token spend per agent type (last hour)
ReAct Debug Agent4,821 tok · $0.004
Planning Feature Builder12,450 tok · $0.009
Reflection Code Review8,234 tok · $0.007
Multi-Agent Feature Squad38,112 tok · $0.028
Sequential Deploy Pipeline2,100 tok · $0.001
Headless Dep Updater6,780 tok · $0.005
Total (this hour)72,497 tok · $0.054
Context efficiency94%
Cache hit rate67%
Guardrail pass rate99.1%
Skill Library — Reusable Instruction Sets for Recurring Tasks
/pr-desc
PR Description Generator
Analyze git diff → identify changed components → write concise summary → list breaking changes → add testing notes.
Hook on PR open
/deploy-staging
Staging Deploy
Build → test → lint → deploy to staging → notify Slack. Plan Mode required. Checkpoint at each step.
Sequential + Plan Mode
/generate-migration
DB Migration Generator
Analyze schema diff → generate migration file → add rollback → run against dev → validate no data loss.
Planning + Reflection
/scaffold-api
API Scaffold from Schema
Parse schema → generate CRUD routes → add validation → write tests → run lint+typecheck → open PR.
Planning + Multi-Agent
/update-deps
Dependency Updater
Check outdated → filter breaking → run tests → open PR with changelog. Runs nightly via cron headless.
Headless + Cron
/explain-error
Error Explainer (Slack Bot)
DM /explain-error [message] → ReAct agent searches codebase, logs, and docs → synthesizes fix suggestion.
ReAct + MCP + Slack
7-Day Action Challenge
DAY 1
Create CLAUDE.md
Add your top 3 conventions. Notice the difference in your next session. Zero re-explaining.
DAY 2
Set up Permissions
Block one risky command you've worried about. rm -rf, git push --force, DROP TABLE.
DAY 3
Define Slash Command
One slash command for your most frequent task. Use it twice. Notice the time saved.
DAY 4
Enable Plan Mode
Use Plan Mode for a non-critical task. Experience approving before execution. No more anxiety.
DAY 5
Add a Hook
Hook that runs your linter after every file edit. Watch automation kick in automatically.
DAY 6–7
Try Compaction + MCP
Compact a long conversation. Then sketch how MCP, Subagents, or Headless could automate a recurring team task.
Active Spans
1,847
Last 60 seconds
Avg Span Duration
142ms
p50 across all agents
Error Spans
7
0.38% error rate
Trace Depth (max)
9
Multi-agent orchestration
🕸 Live OpenTelemetry Trace Stream
Every span across all agents — timestamp · trace ID · duration · tokens · status
📊 Span Latency Distribution
p50 / p90 / p99 by agent pattern type
ReAct Agentsp50: 95msp90: 340msp99: 1.2s
Planning Agentsp50: 280msp90: 820msp99: 2.8s
Reflection Agentsp50: 420msp90: 1.4sp99: 4.1s
Multi-Agent (coord)p50: 180msp90: 640msp99: 3.4s
Tool Calls (external)p50: 42msp90: 180msp99: 890ms
Trace Topology
orchestrator → react-agent → tool:read_file
→ tool:run_tests
→ planning-agent → sub:schema
→ sub:api-gen
→ sub:test-gen
→ reflect-critic → ✓ guardrail pass
→ tool:run_tests
→ planning-agent → sub:schema
→ sub:api-gen
→ sub:test-gen
→ reflect-critic → ✓ guardrail pass
Agent Call Graph — Inter-agent communication events (last 10 min)
🎼
Orchestrator
847 calls out
0 errors
🔄
ReAct Cluster
3,214 tool calls
3 timeouts
🗺
Planning Cluster
412 sub-tasks
1 plan revision
🛡
Guardrail Layer
4,473 checks
12 blocked
Faithfulness
0.94
RAG answer vs context
Answer Relevancy
0.91
Response vs question
Context Precision
0.88
Retrieved chunks quality
Context Recall
0.86
Coverage of ground truth
📐 RAGAS Scores — Per Agent (Live)
Faithfulness · Relevancy · Precision · Recall — updated every 60s
| Agent | Faith. | Relev. | Prec. | Recall | Trend |
|---|---|---|---|---|---|
| RERA Compliance | 0.97 | 0.94 | 0.91 | 0.89 | ▲ +0.02 |
| Debug ReAct | 0.89 | 0.93 | 0.82 | 0.78 | — flat |
| Code Review Reflect | 0.96 | 0.90 | 0.88 | 0.92 | ▲ +0.04 |
| Feature Squad | 0.81 | 0.87 | 0.79 | 0.74 | ▼ -0.03 |
| Doc Writer | 0.95 | 0.92 | 0.93 | 0.88 | ▲ +0.01 |
| Alert Triage | 0.88 | 0.94 | 0.85 | 0.83 | ▲ +0.02 |
🔬 Eval Metrics Explained
What each RAGAS metric measures and when to act
📎
Faithfulness
Is the answer grounded in retrieved context? Low score = hallucination risk. Act below 0.85.
🎯
Answer Relevancy
Does the answer address what was actually asked? Low = agent is answering a different question.
🔍
Context Precision
Are retrieved chunks actually relevant? Low = retrieval is pulling noise. Fix: re-rank, better chunking.
📚
Context Recall
Did retrieval find ALL the relevant info? Low = ground truth missing. Fix: corpus coverage, k-nearest.
⚠ Action Needed
Feature Squad context recall dropped to 0.74 — below 0.80 threshold. Retrieval likely missing relevant context. Recommend: increase k from 5→8, add hybrid BM25+vector retrieval.
Eval Run History — Last 24 hours
00:00
0.93
pass
04:00
0.94
pass
08:00
0.81
warn
12:00
0.92
pass
16:00
0.91
pass
Now
0.94
live
Rules Active
47
Domain-specific rulesets
Checks Today
14,821
All output validated
Blocked
38
Routed to human queue
Pass Rate
99.7%
Above 99% SLA
🛡 Active Guardrail Rules
NeMo Guardrails + custom domain rulesets — version controlled
Token spend limit per call (< 4,000)ACTIVE14,783 pass
Max reasoning loops per agent (≤ 5)ACTIVE12,441 pass
Citation required for compliance answersACTIVE3,214 pass · 12 fail
No PII in output (name, email, SSN)ACTIVE14,821 pass
Hallucination detection (NLI score > 0.85)ACTIVE14,797 pass · 24 flag
Block: rm -rf, git push --force, DROP TABLEACTIVE0 attempts
Output schema validation (typed objects only)ACTIVE14,819 pass · 2 fail
Human escalation threshold (< 0.7 confidence)ACTIVE38 escalated
Security: no secrets/tokens in outputACTIVE14,821 pass
Context window budget (≤ 80% used)ACTIVE14,644 pass · 177 warn
🚦 Recent Guardrail Events
Blocked outputs, human escalations, policy violations
Citation missing — RERA compliance answer
RERA Compliance Agent generated a regulatory answer without citing the source document. Rule: citation_required_for_compliance.
Loop limit approaching — Debug Agent
ReAct Debug Agent on loop 4/5. Approaching max reasoning loops. Will escalate to human if no resolution on next step.
Context window at 84% — Feature Builder
Planning Feature Builder context usage above 80% budget. Compaction triggered. 12,400 tokens compressed to 1,800.
Human review resolved — Escalation #37
Engineer approved ambiguous compliance interpretation. Decision logged to episodic memory as precedent for future similar queries.
Hallucination flagged — NLI score 0.71
Doc Writer Agent output had NLI faithfulness score below 0.85 threshold. Reflection loop triggered. Output revised and re-evaluated.
Guardrail Configuration
⚙️ Policy Toggles
Runtime-configurable rules — changes take effect immediately
Require citations for compliance
Auto-escalate low confidence (<0.7)
Hallucination detection (NLI)
PII scrubbing in outputs
Context compression at 80%
Reflection loop on low eval score
📋 Audit Trail
Every guardrail decision logged and exportable
18:42:11 BLOCK rera-compliance · citation_required · conf=0.61
18:41:03 WARN react-debug · loop_limit=4/5 · monitoring
18:39:47 COMPR planning-feature · ctx=84%→23% · 10.6k tok saved
18:35:22 RESOL escalation#37 · human-approved · 4m12s
18:28:54 RFLCT doc-writer · nli=0.71 · triggered reflection loop
18:22:11 PASS all-agents · batch-check · 847 outputs validated
18:41:03 WARN react-debug · loop_limit=4/5 · monitoring
18:39:47 COMPR planning-feature · ctx=84%→23% · 10.6k tok saved
18:35:22 RESOL escalation#37 · human-approved · 4m12s
18:28:54 RFLCT doc-writer · nli=0.71 · triggered reflection loop
18:22:11 PASS all-agents · batch-check · 847 outputs validated
Working Memory
28K
Avg tokens in context
Episodic Store
4,821
Past decisions logged
Semantic Index
2.1M
Vectors in RAG corpus
Cache Hit Rate
67%
Saved 840K tokens today
🧠 Memory Tiers
Hot → Warm → Cold — cost increases with retrieval depth
Working Memory (Hot)
Active deal state, current task context, last 5 tool outputs. In context window. Billed every call.
28K tok
always in context
Session Memory (Warm)
Conversation history, prior agent outputs, compacted summaries. Retrieved on demand. ~200 tokens when loaded.
847 items
retrieved on demand
Episodic Store (Cold)
Past compliance decisions, resolved bug patterns, precedents. RAG-retrieved. Only loaded when explicitly needed.
4,821
episodic records
Semantic Index (RAG)
Live GujRERA filings, GST notifications, codebase chunks, docs. 2.1M vectors. Hybrid BM25 + dense retrieval.
2.1M
indexed vectors
📊 Memory Cost Analysis
The boundary between tiers is where most cost is generated
💸
Context Inflation Risk
Retrieving 5 docs × 2,000 tokens each adds 10,000 tokens to every subsequent LLM call in that session. Long-term memory cost is at the transition boundary, not in storage.
✂️
Compaction Strategy
3,000-token conversation → 200-token typed summary object. Compaction at 80% context budget. 10.6K tokens saved today via compaction alone.
🎯
Selective Retrieval
RERA agent running ≠ load buyer conversation history. Different task = different info need. Retrieved context is task-scoped, not session-global.
⚡
Semantic Caching
Same RERA query by different agents = cache hit, zero embedding cost. 67% hit rate saved 840K tokens ($0.63) today. Cached entries expire after 24h for regulatory freshness.
Memory Budget per Agent (today)
ReAct Debug34K tok
Planning58K tok
Multi-Agent82K tok
Reflection48K tok
Critical Anomalies
3
Require immediate action
Warnings
8
Monitoring closely
MTTR (avg)
4m 12s
Mean time to resolve
Auto-resolved
94%
Without human intervention
Active Anomalies — AI-detected deviations from baseline behaviour
🔴 Token Spike — Feature Squad Agent
18:42 · 3 min ago
Feature Squad Multi-Agent coordinator consumed 38,112 tokens in the last 15 minutes — 4.2× above baseline of 9,000 tokens. Root cause: subagent-3 (API generator) is looping on schema parsing. Context is inflating with each unsuccessful tool call. Estimated cost overrun: +$0.023/hr if unchecked.
Baseline: 9K tok/15min|Current: 38.1K tok|Loop count: 7/5 (limit exceeded)
🔴 RAGAS Score Drop — Context Recall 0.74
17:58 · 44 min ago
Feature Squad agent context recall dropped from 0.91 baseline to 0.74 — below the 0.80 alert threshold. Retrieval is missing relevant context chunks. Likely cause: corpus staleness or chunking mismatch after recent repo refactor. 3 compliance answers in this window may be incomplete.
🟡 Latency Degradation — p99 at 4.1s
18:31 · 11 min ago
Reflection Agent p99 latency increased from baseline 2.8s to 4.1s. Three consecutive critique cycles running on the same doc-writer output. Critic and generator are too aligned — producing near-identical outputs on each loop. Reflection not converging.
Auto-resolved in last 24h
Context Budget Warning
Planning agent at 84% context → compaction auto-triggered → resolved in 2.3s. 10.6K tokens saved.
18:39 · auto-resolved
Tool Call Timeout
External API timeout (GitHub) → ReAct agent detected, retried with exponential backoff → resolved in 3 retries.
16:12 · auto-resolved
NLI Hallucination Flag
Doc Writer NLI score 0.71 → reflection loop triggered → score improved to 0.94 → output approved.
18:28 · auto-resolved
Mesh Nodes
47
Active agent instances
Active Edges
124
Live inter-agent calls
Coordinator Calls
847
Routing events / hr
Mesh Throughput
284/hr
Completed workflows
🌐 Agent Mesh Topology — Live Communication Graph
Node size = call volume · Edge brightness = active communication · Dashed = idle
🔀 Routing Logic
Orchestrator uses deterministic routing for known task types (deploy, PR-desc, scaffold). LLM routing only for novel tasks — reduces routing failures and cost.
📡 Communication Protocol
All inter-agent messages are typed structured objects — not raw text. Every handoff is a logged event with timestamp, source, target, payload size, and latency.
🚫 No Peer-to-Peer
Agents never communicate directly with each other. All messages route through the orchestrator. Prevents circular loops, cost spirals, and untracked state mutations.
Pipelines Today
47
AI-augmented runs
Avg Pipeline Time
4m 12s
Down 68% with AI
AI-caught Bugs
23
Before production
Auto-fixed
18
No human needed
Live Pipeline Run — PR #251 · Feature: User Profile
✓
Checkout
2s
2s
→
✓
AI Code Review
47s
47s
→
✓
Build + Lint
1m 12s
1m 12s
→
⟳
AI Test Gen
running...
running...
→
🔒
Security Scan
queued
queued
→
🚀
AI Deploy
queued
queued
→
📊
Ops Monitor
queued
queued
Step 4 — AI Test Generation (active): Reflection agent analyzing PR diff. Generating edge case tests for auth flow, null user profile, concurrent request handling. Target: 85% branch coverage. ETA: ~45s.
🤖 AI Steps in this Pipeline
Where agents augment the traditional CI/CD flow
📄
AI Code Review (step 2)
Reflection agent generates review, critiques own output, posts to PR. Caught 2 null-pointer risks, 1 missing auth check.
🧪
AI Test Generation (step 4)
Planning agent decomposes diff into test cases. Reflection loop ensures edge cases covered. CX associates' test PRs validated here.
🔒
AI Security Scan (step 5)
ReAct agent runs pen tests, checks OWASP top 10, scans for secrets in diff. 50% of vulns auto-patched (library version bumps).
🚀
AI Deploy Agent (step 6)
Sequential agent: build → test → deploy to staging → smoke test → promote to prod on human approval. Checkpoint at each step.
📈 Pipeline Metrics (last 30 runs)
AI augmentation impact on speed and quality
Avg pipeline time13m 20s→ 4m 12s
Bugs caught pre-merge4.2/run→ 12.1/run
Test coverage62%→ 89%
Prod incidents from CI2.1/wk→ 0.3/wk
Engineer review time45 min→ 8 min
Non-eng PR contributions0%→ 23%
Active Workflows
12
Running now
State Transitions
847
Last hour, all logged
Rollbacks Available
284
Checkpoint snapshots
Temporal Workers
8
Durable execution nodes
🎼 LangGraph State Machine — Deal Lifecycle
Current state of active workflow: PR #251 · Feature Squad
Graph: feature-squad-v2 · run_id: fs-20260518-1842
✓ START → ✓ parse_task → ✓ spawn_agents
├─ ✓ agent_1_research
├─ ✓ agent_2_schema
├─ ⟳ agent_3_api_gen (loop 4/5)
├─ ◦ agent_4_ui (waiting)
└─ ◦ agent_5_tests (waiting)
→ ◦ synthesize → ◦ guardrail_check → ◦ END
⚠ agent_3 approaching loop limit — conditional edge to human_review if loop 5 fails
⚙️ Temporal Workflow State
Durable execution — every state persisted, retries deterministic
workflow_id: deploy-staging-1842
COMPLETED npm run build · 1m 12s
COMPLETED jest --coverage · 47s
RUNNING eslint src/ · 12s...
PENDING vercel --staging
PENDING notify-slack
checkpoints: 3 saved · retries: 0 · timeout: 10m
Why Temporal, not LLM planning
Temporal owns the deterministic spine — known steps, durable execution, retry logic, audit log. LangGraph manages the AI agent topology at unstructured edges. LLM planning is reserved for edge cases — not for every deploy step that is already defined in code.
Workflow Checkpoint Registry — Rollback any step instantly
18:42:11
pre-deploy-1842
Before staging deploy. All tests passing.
18:39:47
post-compaction-1840
After context compaction. 10.6K tokens freed.
18:28:54
pre-reflection-loop-1835
Before NLI score 0.71 reflection triggered.
17:58:00
session-start-1820
Clean session start. All agents freshly initialized.