ADLC Strategy & Marketplace Prototype
Summary
Freelancer Client is hiring: ADLC Strategy & Marketplace Prototype.
Location: Remote
I need help formalizing an Agentic Development Life Cycle (ADLC) Strategy that places efficiency at the center of every phase, from requirements capture to deployment and maintenance. The goal is a lean, repeatable framework that cuts wasted time and hand-offs while still giving room for future growth.
Metadata (capabilities, inputs, outputs) 2. Agent Packaging Standard Each agent is packaged with:
Think of ADLC as the equivalent of SDLC, but tailored for AI agents instead of traditional apps.
Alongside the written strategy, I also want a clickable marketplace prototype that lets stakeholders see how the ADLC principles translate into a real product flow. At minimum the prototype must cover navigation and core interactions; I am open to your recommendation.
Now think of this as the distribution layer for ADLC outputs.
Skills: Project Management, Artificial Intelligence, Content Strategy, Documentation, Enterprise Architecture, Prototyping, Agile Project Management, AI Agents, Design Thinking, Agentic AI
Budget: $30–$250 USD
Source: Freelancer Client via Remote / Online. Apply on the source website.
Original
I need help formalizing an Agentic Development Life Cycle (ADLC) Strategy that places efficiency at the center of every phase, from requirements capture to deployment and maintenance. The goal is a lean, repeatable framework that cuts wasted time and hand-offs while still giving room for future growth.
1. ADLC — Agentic Development Lifecycle (Strategy)
Think of ADLC as the equivalent of SDLC, but tailored for AI agents instead of traditional apps.
Where SDLC = build software
ADLC = build autonomous decision-making systems
Core Idea
An Agentic Development Lifecycle defines how you:
Design agents (roles, goals, tools)
Build them (prompts, workflows, integrations)
Evaluate them (accuracy, safety, performance)
Operate them (monitoring, memory, improvement)
Typical ADLC Phases (practical version)
1. Define (Intent & Scope)
What is the agent supposed to do?
Define:
Inputs
Outputs
KPIs
2. Design (Agent Architecture)
Single agent vs multi-agent system
Tooling:
Define:
Memory (short-term / long-term)
Tool interfaces (Lambda, APIs)
Decision boundaries
3. Build (Agent + Tools)
4. Evaluate (AI Eval + Performance + Red Teaming)
This is where ADLC differs heavily from SDLC.
You must test:
✅ Functional correctness
✅ Decision quality
✅ Safety / hallucination
✅ Regression vs baseline
✅ Cost (tokens, runtime)
Examples:
Did RCA correctly identify root cause?
Did agent call the right tool?
Did it miss a regression?
5. Deploy (Runtime + Orchestration)
6. Observability (Telemetry & Monitoring)
Key difference:
You monitor decisions, not just system health.
Improve (Feedback Loop)
Retrain prompts
Add tools
Tune thresholds
Update evaluation datasets
Continuous learning loop
Key Insight
ADLC = SDLC + ML lifecycle + Observability + AI Evaluation
Alongside the written strategy, I also want a clickable marketplace prototype that lets stakeholders see how the ADLC principles translate into a real product flow. At minimum the prototype must cover navigation and core interactions; I am open to your recommendation.
2. Agentic Marketplace Prototype
Now think of this as the distribution layer for ADLC outputs.
Core Idea
A marketplace where teams can:
Discover agents
Reuse tools
Share workflows
Plug into existing systems
What goes into an Agent Marketplace?
1. Agent Registry
Like:
GitHub Packages
Docker Hub
MCP Registry (what your company is already doing)
Stores:
Agent definitions
Metadata (capabilities, inputs, outputs)
Versions
2. Agent Packaging Standard
Each agent is packaged with:
Manifest (YAML/JSON)
Tools it uses
Required permissions
Input/output schema
3. Discovery & Search
Search by:
Use case (RCA, testing, chatbot)
Domain (telco, e-commerce)
Tools (Datadog, k6)
4. Execution Integration
Plug into:
Bedrock AgentCore
Azure AI Foundry
Internal MCP Gateway
5. Access Control
Who can use what agent
RBAC / IAM / Entra ID integration
6. Ratings / Evaluation Layer
Performance scores
Accuracy benchmarks
Cost metrics
Marketplace Prototype (Your Context)
Given your setup, a strong prototype could look like:
Backend
Agent registry (GitHub + internal registry)
Metadata store (DynamoDB / CosmosDB)
API layer (Node/Python)
Integration Layer
UI
Browse agents
Deploy agents
View performance metrics
How ADLC + Marketplace Work Together
ADLC = how you build agents
Marketplace = how you scale and reuse them
Think:
ADLC produces “agent artifacts”
Marketplace distributes them across teams
What You’re Really Building (Strategic View)
Please base your work on widely adopted best practices—think BPMN, agile artefacts, and clear RACI mapping—so the final package can be rolled straight into an enterprise environment.
Deliverables
• ADLC Strategy and playbook (overview, detailed phase activities, entry/exit criteria, KPIs)
• Process diagrams and swim-lanes highlighting efficiency gains
• Interactive marketplace prototype, hosted or share-link ready
• Short hand-over guide explaining how to evolve both artefacts after this engagement
I will review against clarity, coherence, and the ease with which a team unfamiliar with ADLC can pick it up and run.
Location & Details
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