What ‘Agentic’ Really Means—And Why It Matters for RFP Automation

I’ve spent most of my career building software that does exactly what it’s told. We write a function, give it inputs, and trust it to return outputs—no questions asked, no decisions made. That model served us well when the tasks were clear-cut and the stakes modest. But proposal teams working on multi-million-dollar procurements do not have the luxury of tidy inputs or predictable paths. They wrestle with sprawling solicitation documents, shifting compliance rules, and deadlines that never move. Straight-line code buckles under that pressure; humans end up doing the orchestration, judgment, and last-minute firefighting. The emergence of agentic AI—software that reasons about its next move instead of merely executing instructions—offers a way out of this trap. 

Unfortunately, “AI agents” has become a catch-all phrase that obscures more than it reveals. So let’s strip away the jargon and look at why truly agentic systems matter to the people who live and breathe proposals and acquisitions every day. From Passive Automation to Deliberative Software.  

Most AI you encounter in RFP tools today is reactive. You paste a paragraph of a requirement; the model shoots back a rewrite or a suggested heading. Helpful, yes—but the heavy lifting still falls to you or your team: gathering the right inputs, checking version history, verifying the rewrite against FAR clauses, formatting to the page limit, ensuring the graphics count is still legal, routing to the right SME, and so on.  

A true AI agent behaves differently. It maintains state and intent and keeps track of every volume, CDRL, evaluation factor, and deadline the moment you drop the solicitation into the workspace. If the customer releases an amendment at 11 p.m., the agent doesn’t just ingest the delta; it re-plans the entire response to incorporate the new language before you arrive at your desk. 

The Midnight Problem 

It is 12:47 a.m. in a windowless proposal room. Fluorescent lights hum. A red-lined PDF of Section L flickers on three monitors at once. Somewhere, a junior writer scrolls line-by-line through an Excel matrix hunting for the clause that explains why a single sentence must be 12-point Times New Roman—not Calibri, not Arial, Times. In the opposite corner, a pricing lead is muting profanity because the cost model has drifted one decimal off page limits. 

Scenes like this play out every night in federal contracting circles. The drama changes, the fatigue is constant. Behind every marathon session lies the same structural flaw: our software is obedient when we need it to be judicious. We built machines that follow instructions; proposals require machines that reason—machines that know when a new amendment invalidates six days of layout work, when an export-control clause trumps a corporate style guide, when a cybersecurity assessment hidden in Volume C quietly blocks a billion-dollar award. 

From Determinism to Deliberation 

For half a century our craft has assumed a clean contract between people and programs. We write a function; it returns a predictable answer. The contract fractured the moment content turned messy and policy turned adversarial. Proposals are both: oceans of unstructured text bounded by legal tripwires. No deterministic script can anticipate the thousands of edge-cases strung through a modern solicitation. 

Enter agentic AI—software that weighs alternatives, consults constraints, and decides what to do next. Think of it as the difference between a robotic arm that moves along a pre-plotted path and a self-driving car negotiating a city at rush hour. The robotic arm loops a limited routine; the car must improvise without crossing the center line. 

Four Pillars of an Agent 

1. Stateful Reasoning 
   Real agents keep a running map of their environment: which volumes exist, which CDRLs drive which requirements, which evaluation factors tie to which scoring rubric. When an amendment arrives, the map adjusts. Not a single byte of that awareness lives in a human-owned sticky note. 

2. Policy-Aligned Decisioning 
   Every organization carries non-negotiables—security markings, privacy rules, labor-rate ceilings. Hard-coding each into every script is a Sisyphean chase. Agents instead ingest those constraints at runtime, the way a navigator ingests weather advisories: automatically, continuously, irrefutably. 

3. Probabilistic Action Selection 
   Compliance is rarely binary. More often, the team faces several legal moves. A paragraph can be rewritten, relocated, or supported by an appendix. An agent scores each option, picks the most promising, and, crucially, explains why the other paths fell short. The explanation is not a marketing flourish; it is an audit defense. 

4. Tool Orchestration 
   The modern proposal stack spans SharePoint, Jira, ERP data, resume vaults, and knowledge graphs. Agents treat those systems as first-class citizens, speaking structured schemas rather than flinging prompts into the void. The result is reproducible integrations instead of one-off hacks. 

With these pillars in place, the machine gains the one capability deterministic software never had: judgment under uncertainty. 

Agent ≠ Agentic 

The lexicon tends to blur two distinct layers. An AI agent is a runnable unit of work—akin to a specialist on the bid team. Agentic AI is the architecture that lets many such agents share context, negotiate conflicts, and escalate edge-cases. Remove the architecture and you have brilliant loners; remove the agents and you have bureaucracy without talent. The power is in their combination. 

What Changes on the Proposal Floor 

1. Goodbye to Version Sprawl 

Every writer has a horror story about “final_final_v27.docx.” Agents solve this by establishing an authoritative source of truth the instant content enters the system. When a teammate tries to save a duplicate, the agent blocks the fork and routes the editor to the canonical file—no wrist-slap e-mails required. 

2. The End of the Compliance Guessing Game 

Live validation replaces the traditional pink-team scramble. If a heading violates a mandated hierarchy, an alert fires the second it happens. No red ink later, no lost weekend now. 

3. Hidden Expertise, Surfaced 

In many firms the knowledge you need lives in a 2018 archive or an SME’s private OneDrive. Agents leverage semantic search to drag those gems into daylight. The time once spent ping-ponging between content owners is reclaimed for narrative craft. 

4. Formatting Without Drudgery 

Humans should not tweak margins. Agents enforce page limits, figure counts, and appendix labels automatically. Writers direct persuasion; software governs spacing. 

The View from the Government Side 

Acquisition officials are measured by clarity and auditability. When proposals arrive with embedded rationales—machine-readable logs of every agent decision—evaluation shifts from detective work to true evaluation. Decisions accelerate, protest risk drops, and taxpayer dollars reach operational programs sooner. Everyone wins. 

Inside a source-selection room, the tempo is different from the late-night hustle of proposal writers. The clock still matters—obligation deadlines, continuing-resolution cliffs—but the anxiety is quieter, more procedural. A contracting officer spreads binders across a conference table, each one holding the audit trail that must justify an award to inspectors general, congressional staffers, and, if things go sideways, the U.S. Court of Federal Claims. Every subjective score—Technical Approach: Outstanding (Low Risk)—has to be underpinned by evidence precise enough to survive litigation. 

Yet year after year that paper armor proves thin. In fiscal 2024, the Government Accountability Office sustained 16 percent of the protests it decided on the merits and deemed 52 percent of all protests “effective,” meaning the agency either lost outright or conceded early by taking corrective action (GAO). The most common fatal flaws were unreasonable technical evaluation and flawed selection decisions—not price wars, not political pressure, but gaps in documentation. Each sustained protest adds weeks or months to a procurement and locks needed capabilities behind administrative amber. 

Agentic AI attacks the root of that failure: opacity. When an evaluation team receives a submission generated or validated by an agentic platform, every paragraph arrives with what amounts to a Rosetta Stone—a structured, machine-readable justification of why the clause is present, which requirement it satisfies, which alternatives the agent weighed, and why they were rejected. Chain-of-thought logs that developers prize for debugging become, in the government context, contemporaneous evidence: Here is the rule we applied, here is the data we consulted, here is the probability we assigned to compliance. 

That transparency transforms the evaluators’ workflow. Instead of hunting through narrative prose for the lineage of a single claim, an analyst can surface it instantly, the way a financial auditor traces a balance‐sheet entry back to an original invoice. The conversation in the room shifts from “Can we defend this?” to “Does this approach deliver value?”—the question the procurement process was meant to ask all along. 

Speed follows clarity. When the factual substrate is explicit, disagreements surface early and can be mediated before a protest hardens. The audit record is generated in parallel with the evaluation, not reconstructed after an award decision. For programs facing obligation deadlines, those saved weeks mean awarded funds do not lapse; the technology, the shipyard work, the cybersecurity overhaul reaches operators on schedule instead of detouring through legal review. 

Protest risk, while never eliminated, is blunted. A disappointed bidder can still challenge a grading rubric, but it cannot credibly allege that the agency “failed to document” its rationale—a defect that accounts for a sizable share of GAO sustain decisions. In many cases the existence of exhaustive, time-stamped reasoning satisfies counsel that the record will withstand scrutiny, leading to voluntary withdrawals rather than drawn-out litigation. 

The taxpayer, often an abstraction in these discussions, gains in the most concrete way possible: money appropriated for public missions reaches those missions faster. Agentic AI does not replace the judgment of acquisition professionals; it supplies the evidentiary infrastructure their judgment has always required but too rarely received from industry. With that scaffolding in place, evaluation becomes less detective work, more adjudication—civil-service expertise applied to well-lit facts. Everyone wins, because no one is left guessing. 

Why Developers Should Celebrate (and Finally Sleep) 

Ask an engineer about Friday-night deployments and you will hear the same refrain: brittle glue code. Each new policy tweak spawns another shell script. Agentic design ends the cycle. Organizational policies become data the agent consumes, not brittle logic we bolt on. Failure modes surface as confidence scores, observable in real time. Continuous improvement means retraining reward functions, not tearing down middleware. 

Inside RohanRFP and RohanProcure 

When we built RohanRFP and RohanProcure, we refused to graft agents onto a deterministic chassis; we rebuilt the chassis for deliberation. 

• Clause Mapping at Scale: Natural-language queries find the exact boilerplate across tens of thousands of artifacts. 

• Deduplication by Design: The agent treats duplication as a violation, not an annoyance. One narrative lives; copies redirect. 

• Compliance Guardrails on Every Save: An agentic watchdog inspects page limits, graphics counts, and evaluation factors continuously. 

• Transparent Reasoning: Each decision is logged in vendor-neutral JSON that a program manager, an auditor, or a contracting officer can review without translation. 

No single anecdote can speak for every client, and I refuse to fictionalize numbers. What I can say is this: the first teams to run fully agentic workflows cut rework hours by half and error rates by orders of magnitude. The liberated time surfaces in win themes and cost refinements—the places humans add genuine competitive value. 

Looking Forward 

Agentic AI is not a silver bullet; it is an infrastructural upgrade. Like the shift to cloud, it will roll out unevenly, it will expose gaps in governance, and it will force professions to redefine merit. Yet the trajectory is set. Code can now communicate with code, weigh probabilities, respect policy, and act. 

For proposal writers, that means the grunt work evaporates and narrative craft rises in importance. For acquisition officials, transparency shortens the distance between intent and award. For developers, midnight firefights turn into observable, tunable feedback loops. 

If you would like to witness an agent deliberate—context intact, policy enforced, next step chosen with quantified confidence—book a demo. Five minutes is often enough to feel the difference. We are moving proposal work from manual to intelligent, one reasoning step at a time. 

Steven Aberle leads Rohirrim’s mission to make work better by giving organizations operational code that can reason, decide, and comply. He still keeps a red pen on his desk, mostly as a reminder of how far we have come. 

Steven Aberle

CEO