Agentic Development
Production agents. Not demo chatbots.
Principal-led agentic development for teams that need autonomous workflows, orchestrated agents, and full-stack applications in production, not POC theater, slide decks, or wrapper apps that stall in pilot.
What we build
Agentic systems with explicit tool interfaces, human-in-the-loop gates, and observability from day one. Part of our broader AI consulting practice, same principal, same Production Standard.
Autonomous workflow agents
Multi-step agents that query systems, draft outputs, route work, and complete bounded tasks, with human approval gates before irreversible actions.
Tool-first architecture
Typed tools with input validation, rate limits, and audit logging. No raw database access, no unrestricted API keys.
Evaluation harnesses
Test suites for expected tool sequences and outputs before production. Agent regressions caught on every model or prompt change.
Observability & tracing
Full step-level logs: plan, tool call, result, retry, final output. Agent runs treated like distributed transactions.
Legacy modernization
Agentic code analysis, incremental refactoring, and platform upgrades, production paths without multi-year rewrites.
Full-stack delivery
Agents embedded in web apps, APIs, and internal tools. One principal architects, builds, and deploys end to end.
Production use cases in 2026
Agents succeed in bounded domains with clear tool interfaces and measurable outcomes. See our enterprise agentic AI playbook for architecture patterns and failure modes.
Agentic vs. chatbot
RAG chatbot
"What's our refund policy?" → retrieve documents → generate answer. Useful, bounded risk, often the right tool.
Production agent
"Process this refund request" → verify order → check policy → initiate refund → notify customer → log action. Requires tool design, approval gates, and full observability.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Further reading
Deep dives on agentic architecture, anti-slop frameworks, and production lessons.
Strategy
When NOT to Build an Agent: A Decision Tree for Enterprise Teams
Most agent RFPs we review should be retrieval pipelines, rules engines, or simple automation. Here's the decision framework we use in week one to redirect budget away from agent complexity and toward what actually ships.
Technical
Agent Evaluation Harnesses: How to Test Agents Before Production
Chatbot evals won't catch agent failures. Production agent systems need test suites for tool sequences, idempotency, approval gates, and regression on every model change. Here's how we build evaluation harnesses that actually prevent incidents.
Strategy
Agentic AI in the Enterprise: Beyond Chatbots to Autonomous Workflows
2026 is the year agents move from demos to production. We outline the architecture patterns, guardrails, and use cases where autonomous AI agents deliver ROI, and where they still fail.
Strategy
The AI Slop Manifesto: Why Most Enterprise AI Projects Fail Before Production
POC theater, wrapper apps, and slide-deck deliverables are eating enterprise AI budgets. Here's our Production Standard, a six-point contract against AI slop, and the checklist we use to redirect or ship.
Strategy
LLMs in Production: Lessons from the First Wave
After deploying large language models for enterprise clients, here's what we've learned about prompt engineering, retrieval-augmented generation, and the true cost of running these systems.
Strategy
The Production Gap: Why 87% of ML Projects Never Make It to Deployment
After analyzing hundreds of enterprise AI initiatives, we've identified the three critical failure points that kill most ML projects before they deliver value, and the architectural decisions that prevent them.
Client perspective
PrismBase didn't hand us a slide deck. They shipped a fraud detection pipeline that processes our full transaction volume in real time. We went from months of stalled POCs to production in six weeks.
“The DB Optimizer audit found three missing indexes that were killing our API latency. We cut p95 response time by 60% within a week of implementing their recommendations.”
VP Engineering
B2B SaaS Platform
Enterprise SaaS
“Finally, an AI consultancy that tells you what won't work. They saved us six months by redirecting our LLM initiative toward a simpler retrieval pipeline that actually shipped.”
CTO
Growth-Stage Startup
B2B Software
Client names withheld under NDA where required. Read documented outcomes
The Principal
Drake TalleyFounder & Principal
Nine years in data, six in production DS/ML/AI—data and ML pipelines in Python and SQL, federal-scale fraud at 10TB+/day, geospatial and time series analytics, biotech R&D under NDA, and governed LLM deployments. Same principal from the first call through production.
- , Federal fraud detection at 10TB+ daily throughput
- , Biotech and life-sciences under full NDA
- , Production Standard contracted in writing on every mandate
Agentic development FAQ
What is agentic development?
Agentic development is the practice of building AI systems that plan multi-step tasks, call tools (APIs, databases, code), evaluate results, and iterate until work completes, under guardrails and human oversight. It goes beyond RAG chatbots to autonomous workflows that take action in your systems.
How is agentic development different from building a chatbot?
A chatbot retrieves context and generates answers. An agent uses an LLM as a reasoning engine to orchestrate tools across multiple steps. Complexity, risk, and engineering requirements increase significantly, which is why most 'agent' projects fail without production-grade architecture.
When should we NOT build an agent?
If a retrieval pipeline, rules engine, or simple automation solves the problem, build that instead. We redirect budget in week one when agents add complexity without proportional value, the first commitment in our Production Standard.
How does PrismBase deliver agentic systems?
Principal-led delivery: one expert scopes, architects, codes, and deploys. Every engagement includes tool design, evaluation harnesses, observability, human-in-the-loop gates, and runbooks your team owns. Scoped projects target production in 6–10 weeks.
What does agentic development cost?
Scoped agent mandates start around $15k. Embedded principal partnerships from $25k/month. Start with a $2,500 DB Optimizer audit or free discovery call to establish fit before a larger engagement.
Book your free call
30 minutes. We'll tell you if an agent is the right tool, or redirect you to what will ship.
Free 30-min call · No obligation · or send a message instead
Ship agents. Skip the slop.
Every month in pilot purgatory is a month your competitors deploy production systems. Let's scope what actually ships.
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